GDGB: A Benchmark for Generative Dynamic Text-Attributed Graph Learning
Authors: Jie Peng
Jiarui Ji
Runlin Lei
Zhewei Wei
Yongchao Liu
Chuntao Hong
Venue: ICLR 2026 Poster (2026)
Keywords: Dynamic Text-Attributed Graph
Dynamic Graph Generation
Primary area: datasets and benchmarks
TL;DR: We propose Generative DyTAGs Benchmark (GDGB), addressing poor textual quality in existing datasets and defining novel tasks (TDGG/IDGG) to advance robust and reproducible DyTAG generation research.

Abstract: Dynamic Text-Attributed Graphs (DyTAGs), which intricately integrate structural, temporal, and textual attributes, are crucial for modeling complex real-world systems. However, most existing DyTAG datasets exhibit poor textual quality, which severely limits their utility for generative DyTAG tasks requiring semantically rich inputs. Additionally, prior work mainly focuses on discriminative tasks on DyTAGs, resulting in a lack of standardized task formulations and evaluation protocols tailored for DyTAG generation. To address these critical issues, we propose \underline{G}enerative \underline{D}yTA\underline{G} \underline{B}enchmark (GDGB), which comprises eight meticulously curated DyTAG datasets with high-quality textual features for both nodes and edges, overcoming limitations of prior datasets. Building on GDGB, we define two novel DyTAG generation tasks: Transductive Dynamic Graph Generation (TDGG) and Inductive Dynamic Graph Generation (IDGG). TDGG transductively generates a target DyTAG based on the given source and destination node sets, while the more challenging IDGG introduces new node generation to inductively model the dynamic expansion of real-world graph data. To enable holistic evaluation, we design multifaceted metrics that assess the structural, temporal, and textual quality of the generated DyTAGs. We further propose GAG-General, an LLM-based multi-agent generative framework tailored for reproducible and robust benchmarking of DyTAG generation. Experimental results demonstrate that GDGB enables rigorous evaluation of TDGG and IDGG, with key insights revealing the critical interplay of structural and textual features in DyTAG generation. These findings establish GDGB as a foundational resource for advancing generative DyTAG research and unlocking further practical applications in DyTAG generation. The dataset and source code are available at \url{https://github.com/Lucas-PJ/GDGB-ALGO}.

Published as a conference paper ICLR 2026
GDGB: A BENCHMARK FOR GENERATIVE DYNAMIC TEXT- ATTRIBUTED GRAPH LEARNING
Jie Peng' Jiarui Ji
Runlin Lei
Zhewei Wei!*
Yongchao Liu
Chuntao Hong"
Renmin University of China
Ant Group
(peng_jie, jijiarui, runlin lei, zheweil @ruu. edu. (yongchao. ly, chuntao. het @antgroup.com
ABSTRACT
Dynamic (exti-Atributed Graphs (DyTAGs), which intricately integrate structural, temporal, and textual attributes, are crucial for modeling complex real-world systems. However, most existing DyTAG datasets exhibit poor textual quality, which severely limits their utility for generative DyTAG tasks requiring semantically rich inputs. Additionally, prior work mainly focuses on discriminative tasks on DyTAGs, resulting a lack of standardized task formulations and evaluation protocols tailored for DyTAG generation. To address these critical issues, we propose Generative Dy TAG Benchmark (GDGB), which comprises eight meticulously curated Dy DyTAG datasets with high-quality textual features for both nodes and edges, overcoming limitations of prior datasets. Building on GDGB, we define two novel DyTAG generation tasks: Transductive Dynamic Graph Generation (TDGG) and Inductive Dynamic Graph Generation (IDGG). TDGG transductively generates a target Dy TAG based on the given source and destination node sets, while the more challenging IDGG introduces new node generation to inductively model the dynamic expansion of real-world graph data. To enable holistic evaluation, we design multifaceted metrics that assess the structural, temporal, and textual quality of the generated Dy DyTAGs. We further propose GAG- Generller,,, an LLM-based multi-agent generative framework tailored for reproducible and robust benchmarking of DyTAG generation. Experimental results demonstrate that GDGB enables rigorous evaluation of TDGG and IDGG, with key insights revealing the critical interplay of structural and textual features in DyTAG generation. These findings establish GDGB as a foundational resource for advancing generative DyTAG research and unlocking further practical applications in DyTAG generation. The dataset and source code are available at httt:::://g.hab...cc///aa-aaasss
INTRODUCTION
Dynamic structured data is ubiquitous, spanning various domains, such as social networks (Huang et al.) 2022. Sun et al. 2022). recommendation systems (Zhang et al. [2022 Tang et al. (023), and citation networks (Skarding al. 2021 Geng et al. 2022). Notably, real-world dynamic evolution scenarios inherently contain rich (textual) features. For example, user posts and interactions such as comments and reposts on social networks, and consumer reviews of products e-commerce platforms. This motivates the construction of dynamic tcxt-attributed graphs (DyTAGs), which systematically integrate structural/temporal dynamics and textual attributes (Zhang et al. 2024a).
"Zhewei Wei and Yongchao Liu the corresponding authors.

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Published as a conference paper at ICLR 2026
Existing representative DyTAG benchmarks, such as DTGB Zhang et al. 2024a). demonstrate that state-of- the-art Dynamic Graph Neural Networks (DGNNs) achieve significant performance gains in discriminative tasks (e.g., link prediction, node retrieval, and edge classification) by leveraging Dy DyTAGs' textual features.
Growing interest in generative tasks spans computer vision (CV) (Wang et al. 2021b; Raut & Singh| 2024)
and natural language processing (NLP) (Xu et al. 2024. Blease et al. 2024), with increasing demand for graph generation in domains such as drug discovery (Luo et al.) 2021 Martinelli] 2022), graph augmentation frameworks (Miao et al. [002;] He et al. 2025).
(Ding et al. 2022, Meng et al., 2023), and crivacy-preeervin
However, DyTAG generation remains underexplored. For instance, DTGB focuses narrowly simplistic textual relation generation while neglecting the rich interplay of textual, structural, and temporal dynamics
in graph evolution (Zhang et al. 2024a). This gap underscores the increasing criticality of generating
high-quality DyTAGs
The primary obstacle hindering progress in DyTAG generation lies in the absence of hi high-quality, text-rich DyTAG benchmarks. Current datasets and benchmarks face two critical limitations: 1) Lack of high-quality (textual) attributes: Traditional dynamic graph datasets fundamentally lack node/edge features, relying solely on topological and temporal information (Poursafaci et al. (2022)). This scarcity of attributes poses substantial challenges for generative models that require rich feature imputs. Meanwhile, existing DyTAG datasets from DTGB exhibit poor textual quality, with (source) node texts typically limited to usernames or email addresses, lacking semantic richness (Zhang et al. (024a). which severely restricts the development of DyTAG learning. 2) Lack of standardized DyTAG generative task formulations and evaluation protocols: Current dynamic graph generative models mainly rely on structural and temporal information (Gupta et al., 2022 Dey et al., 2024), necessitating the development of new task formulations and holistic evaluation metrics tailored to TYTA'ss textual characteristics. Furthermore, most approaches prioritize direct generation of the final target graph (Zeno al. 1021 Li et al. 2024), diverging from the incremental and expansive growth patterns observed in real-world dynamic graph generation scenarios (Ji et al.] 2025). Hence, to address these issues, we propose the Generative DyTAG Benchmark (GDGB) from the tollowing three aspects: 1) dataset construction, 2)
task & metric definition, and 3) generative framework design.
Our proposed GDGB comprises eight carefully selected and rigorously processed DyTAG datasets covering domains such as e-commerce recommendations, social networks, biographies of celebrities, citation networks, and movie collaboration networks. All datasets include nodes and edges endowed with rich semantic textual information. Thus, the newly proposed datasets resolve the key problems associated with previous dynamic graph datasets, such as poor quality of textual features, which subsequently results in the inability to support DyTAG generation tasks.
Datasets.
Tasks & Metrics. Based on our GDGB datasets, we introduce two novel DyTAG generation tasks: Trans- ductive Dynamic Graph Generation (TDGG) and Inductive Dynamic Graph Generation (IDGG). TDGG generates a target DyTAG based on the given source and destination node sets, maintaining the transductive assumption that all nodes are known as prior knowledge. Different from TDGG, the more challenging IDGG extends the transductive setting by introducing inductive modeling of new node generation during graph evolution, thereby successfully modeling the dynamic expansion of real-world graphs. To holistically assess DyTAG generation, we design multifaceted evaluation protocols that jointly consider topological patterns, temporal dynamics, and semantic quality, including: /) Graph Structural Metric, 2) Textual Quality Metric, and 3) Graph Embedding Metric.
Given the text-rich nature of DyTAGs, we propose GAG-General, an LLM-based multi-agent framework tailored for DyTAG generation tasks, extending the implementation of GAG (Ji et al. (2025). Unlike the bipartite-graph- graph-specific GAG, GAG-General demonstrates superior generalization across diverse DyTAG scenarios, without requiring domain-specift customization. Furthermore, we implement both TDGG and IDGG tasks within GAG-General, integrating the proposed holistic evaluation metrics to ensure reproducible DyTAG generation and robust benchmarking.
Framework.
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Experimental results of GDGB demonstrate that the proposed datasets, tasks, evaluation protocols, and GAG- General framework collectively enable robust benchmarking for DyTAG generation. Key findings highlight the critical interplay between structural and textual features in DyTAG generation, as well as the practical applicability of DyTAG generation in domains such as e-commerce and social network analysis. While the GAG-Genera framework achieves competitive performance in both TDGG and IDGG, the generative pipeline still warrants further refinement to address challenges in both structural fidelity and attribute richness, ultimately enabling high-quality DyTAG generation. Our contributions could be summarized as follows:
First Generative DyTAG Benchmark. To the best of our knowledge, GDGB is the first generative DyTAG benchmark, including eight high-quality datasets tailored for DyTAG generation.
Novel Generative Tasks, Metric, and Framework. We introduce two novel DyTAG generation tasks, TDGG and IDGG, along with multifaceted metrics for holistic evaluation. Furthermore, we propose GAG-General to ensure reproducible and robust benchmarking.
Empirical Insights. We find that both structural and textual features are crucial for DyTAG generation, guiding future generative framework refinement and practical applications.
2
RELATED WORK
Discriminative Dynamic Graph Learning. Recent years have witnessed significant advances in dynamic graph learning over discrete-time dynamic graphs (DTDGss) and continuous-time dynamic graphs (CTDGs). Substantial progress has been made through various DGNNS (Rossi et al., (220)) |eng et al. (025). These models demonstrate strong performance in discriminative tasks, including dynamic link prediction, node retrieval, etc. Concurrently, the community develops diverse benchmarks to standardize evaluation (Poursafaei ct al. 2022b Huang et al. 2024). Notable initiatives include DyGLib (Yu et al. 2023) benchmarking on classical dynamic graph datasets, TGB (Huang et al., 2024) introducing large-scale graphs for scalability chal- lenges, and TGB-Seq (Yi et al., (202)) focusing on complex sequential patterns. However, current benchmarks primarily provide basic structural-temporal information, with limited datasets containing simplistic statistical attributes for nodes or edges (Zhang et al. 2024a). To address this, DTGB (Zhang et al., (024a) proposes
DyTAG datasets and incorporates textual features through BERT embeddings (Devlin et al. 2019). showing measurable performance gains across multiple discriminative tasks (Zhang et al. 2024a). Nevertheless, the information bottleneck caused by transformed BERT embeddings inevitably compromises original semantic richness, leaving open the challenge of effectively utilizing textual semantics for enhanced Dy DyG learning.
Generative Dynamic Graph Learning. Compared to its discriminative counterpart, generative dynamic graph learning is still in its nascent stages. Early approaches primarily focus on DTDGs, including (Zeno 2021: Zhou al. 2020 Dey al. 2024). Recent advances have shifted toward CTDG generation, enabling more refined temporal modeling that better aligns with real-world application needs (Zhang et al.
ett al.
[021 Gupta et al. 2022). Contemporary approaches begin exploring feature-supportive generation: VRDAG (Li et al. 2024) implements node attribute generation through graph-based variational autoencoders (VAEs), while DG-Gen (Hosseini et al. 2024) models edge attributes via joint conditional probability distributions. More recently, GAG (Ji et al., 2025). an LLM-based multi-agent framework, enables simulation- generation for social bipartite DyTAGs. However, this field significantly lacks standardized benchmarks for comprehensive and effective evaluation of dynamic graph generation tasks. First, in terms of task design, existing methods prioritize direct generation of the final target graph (Zeno et al. 2021 Zhou et al. 2020) Li et al., 2024), which deviates from the expansive growth patterns observed in world dynamic graph generation scenarios (Ji et al. 2025). Second, the definition of evaluation metrics lacks multidimensional. holistic assessments of graph structure, temporal dynamics, and attribute coherence (Zhou al. 2020 jet al., [024] Di et al. (025).. Therefore, when extending generative tasks to DyTAG generation, establishing robust benchmark tailored for DyTAG's dynamic and text-rich characteristics becomes critical need.
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Table 1: Statistics of proposed Dy datasets of GDGB. See Section C.1 for more details.
Datuset
Nodes
Edges
Edge Labels
Timestamps
Dumain
Text Attributes
Bipurtite
Sephora
210,351/2,274
801 204
5
5,314
E-commerce
Node & Edge
Dianping
158,541/88,11
1,990.409
745,151
E.commerce
& Edge
WikiRevision
75,622/3.204
2,778.732
2
2,766,153
Web Interaction
Node & Edge
WikiLife
18651-8/54,513
1,996,200
24
Celebrity Biography
Edge
IMDB
125,714
1,534,162
20
122
Movic Collaboration
Node & Edge
X
Weibo'Tech
20,767
109,345
2
79,925
Social Network
Node & Edge
K
WelboDaily
66,500
354,098
2
293,662
Social Network
Node & Edge X
Cora
48,797
110,788
8,274
Citation
Node & Edge X
3 PROPOSED DATASETS
To enable high-quality DyTAG generation, this task necessitates datasets where nodes and edges are accom- panied by rich textual attributes and evolving structure- temporal information over time.
Limitations of Existing Datasets. Existing dynamic graph datasets, though valuable for structural and temporal modeling, suffer from critical limitations in textual attributes that hinder high-quality DyTAG generation. DTGB (Zhang et al., 2024a) represents the first effort to incorporate both node and edge texts. However, as shown in Figure I the node and edge texts in DTGB notably short and semantically shallow in terms of length, perplexity (PPL), and --addded rating. GDGB achieves signif- icant quality advantages over DTGB in five out of six dimensions (see detailed results for each dimension in Section C.5). For in- stance, in six of DTGB's eight datasets, (source) node texts are typically mere identifiers such as email addresses or usernames (Figure [ left). While the two Stack peeckfoo datasets attempt to enrich node texts with user locations and bios, half of these fields remain empty or meaningless. Moreover, edge texts in datasets like GDELT and ICEWS18I 819 are excessively brief (Figure 5 right), providing insufficient context for text generation. Despite DTGB having longer average edge text length, the semantic quality of DTGB's 's texts-at as indicated by PPL and LLM-based rating-is significantly lower than GDGB's. These limitations of current datasets impede progress in DyTAG generation research. A comprehensive comparison of additional existing datasets is provided in Section C.4
Avg Node Length
GDGB DTGB
Text LLM
Node Text PPL
Rating
Avg Edge
Aug Node Text Rating
Text Length
Figure 1: Comparison of node and edge texts across GDGB and DTGB datasets in terms of length, perplexity (PPL (Re- versed)), and LLM-based rating.
Our Proposed High-Quality DyTAG Datasets. To overcome the aforementioned textual quality issues and enable DAT generation tasks, we propose Generative DyT Benchmark, GDGB, consisting of eight carefully curated DyTAG datasets. These datasets are sourced from diverse real-world domains, including e-commerce, social networks. biography networks, etc.. encompassing both bipartite (4 datasets) and non- bipartite (4 datasets) graphs. Detailed statistics are presented Table ! A key focus during the collection and preprocessing of these datasets is to ensure that all source/destination nodes and interaction edges possess rich, semantic textual attributes. For example, Sephora includes: 1) user node texts detailing user appearances and review histories, 2) product node texts describing product brands, ingredients, and ratings, and 3) edge texts containing user detailed reviews. The textual completeness of GDGB provides a robust foundation for developing DATAG generation models capable of producing both realistic structures and coherent text.
Generative Task Performance Validation. To assess the impact of textual quality on dynamic graph generation, evaluate two latest fcature-supportiv
(Li et al. 2024) (node features) and DG-Gen (Hosseini et al. 2024) (edge fcatures) their public implementations. Following DTGB's setup (Zhang et al. 2024a), we encode raw texts with structural fidelity via Degree/Spectra MMD (Xiang et al. 2022) to quantify distributional differences between generated and ground-trun graphs. Lower MMD indicates better structural quality when textual features
(Devlin al. 2019) and measure
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Node Name: Presshdow
TDGG
Joseecs Skia Tong: Light
Name: Foundation Lanotime Price: $53
Loves: 2,128 Price: $52
Review Counte:
Edge Thet
e 20 2023, Rucine: Kevnow Had experiene
00,1992005
Kating: 2
Namte Leunsed Skin Type: Dry
Reviow: Culodt
Review Counte:
(biAn illustration the generation (dashed
IDGG
Geserated Nodes Brand Seplum
Luves: 1,411
textrich seed TAG example Sephora.
Edges of Seed DyTAG
["" 3021, Rating 4,
Geverated Edges
Restew: Good pros, het
Seed DyTAG
Given Nodes in TDGG
Sci Type: Normal
stn Toner Medium fan
Review Counts
Ganerated Edge Aitibutts
(e)An illustration the uodeialge generation (dashedd line) IDGG based on (a)
Figure 2: A case study on TDGG and IDGG in the Sephora product reviews scenario.
are effective. Results in Figures 6 and 7 show that, on GDGB, incorporating node or edge texts significantly reduces MMD,, improving structural similarity. In contrast, DTGB, textual features degrade performance half the cases- especialyy for VRDAG-due to poor text quality. This highlights inherent limitations DTGB. Beyond these experiments, we further validate GDGB's advantages through applying our gen- erative framework (proposed in Section 4.3) to downstream generation tasks, providing a comprehensive demonstration of the advantages and distinctions of GDGB over DTGB in Section
C.5
4
GENERATIVE TASKS, METRICS, AND FRAMEWORK
4.1 GENERATIVE TASK DESIGN
We consider D (W, T), where N denotes the set nodes, denotes the set edges, and denotes the set of timestamps. Let glext and L represent the sets of node texts, edge texts, and edge labels, respectively. For any node n E N, its associated node text is denoted by M Each edge (u, v) e is attached with edge text Elex, edge label Lu,,: and interaction timestamp Te. Leveraging proposed GDGB datasets, we introduce two novel DyTAG generation tasks: Transductive Dynamic Graph Generation (TDGG) and Inductive Dynamic Graph Generation (IDGG).
Transductive Dynamic Graph Generation. Starting with an initial seed DyTAG Go. TDGG generates final DyTAG GK based the given source and destination node sets, maintaining the transductive assumption that all nodes are known as prior knowledge. Thus, the goal of TDGG is to conduct destination node selection and edge generation, which naturally integrates traditional discriminative tasks (e.g., node retrieval and edge classification) in a generative paradigm. The generated DyTAG Gœ in TDGG is expected to maintain structural, temporal, and textual similarity to the ground-truth DyTAG, reflecting high-quality generation. The illustration of TDGG is shown in Figure demonstrating that the user will review the powder at f', along with generated negative review and a rating I.
Inductive Dynamic Graph Generation. Different from TDGG, the more challenging IDGG extends the transductive setting by introducing inductive modeling of new node generation during graph evolution. Starting with initial seed DyTAG Go. IDGG generates final DyTAG Gж based on the dynamically evolving source and destination node sets with newly generated nodes. Due to the incorporation of inductive node generation, the generated Dy DYTAG GK in IDGG is expected to preserve the structural properties of the ground-truth DyTAG, while ensuring that all newly added nodes and edges contain high-quality, semantically coherent textual attributes, thereby successfully modeling the dynamic expansion of real-world graph data.
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The illustration of IDGG is shown in Figure (c), showcasing that the newly generated user will review the newly generated lipstick at t" with generated positive review and rating of 4.
4.2 PROPOSED GENERATIVE METRICS
To ensure a comprehensive assessment of graph structure, temporal dynamics, and textual quality of the generated DyTAG on TDGG and IDGG, we propose multifaceted evaluation protocols for DyTAG generation, integrating structural, textual, and graph embedding-basee metrics. Detailed formulations and implementation specifics the following metrics are provided in Section D.3
Graph Structural Metric. We evaluate the structural validity of generated DyTAGs using two classical approaches: 1) Degree/Spectra MMD: We utilize the maximum mean discrepancy (MMD) with a radial basis function (RBF) kernel to measure the distribution distance between generated and ground-truth graphs (e.g.. degree/spectral properties) (Xiang et al. 2022 You et al., 2018 Chen et al. 2023). 2) Power-law Analysis: We assess power-law behavior in degree distributions using the Kolmogorov-Smimm
distance D2 and the power-law exponent a (Clauset et al., 2009). Specifically, we assess the presence of power law validity by evaluating whether D₁ 0.15 and
[ for the generated graphs
Textual Quality Metric. Inspired by the recent role-playing agent research (Wang et al. 2024b Chen et al. 2024 Wang et al., 2024a), we employ the L.M-as-Evallutoo framework to assess the text quality in the generated DyTAGs. Specifically, the evaluation framework is conducted with five scoring criteria (Contextual Fidelity, Personality Depth, Dynamic Adaptability, Inmersive Quality, Content Richness) tailored for DyTAG generation, scored on a 1-5 scale. Compared to embedding-basee metrics used in prior DyTAG benchmarks (e.g.. BERTScore (Zhang et al.. 2020) in DTGB (Zhang al. 2024a)), this evaluation framework offers two distinct advantages: 1) Unified multidimensional evaluation across diverse textual attributes, and 2) Preservation of semantic fidelity by avoiding information compression inherent embedding representations.
Graph Embedding Metric. To enable a holistic and coupled evaluation of structural, temporal, and textual quality in DyTAG generation, we extend the JL-Metric (Hosseini et al., 2025), designed for dynamic graph generation, to better adaptation on DyTAGs by integrating textual node/edge features, resulting in graph embedding-
indicator that quantifies the generation quality across three critical dimensions: topological patterns, temporal dynamics, and node/edge textual attributes. This metric condenses DyTAGs into a unified embedding space for the pairwise similarity computation between graph embeddings. The resulting scalar score quantifies global fidelity between generated and ground-truth DyTAGs by jointly measuring structural, temporal, and textual characteristics.
4.3 GENERATIVE FRAMEWORK
Limitations of Previous Methods in DyTAG Generation. The novelty of our proposed TDGG and IDGG tasks presents unique challenges, as no existing method can directly address their requirements. For instance, the latest feature-supportiv dynamic graph generation models like VRDAG (Li et al. 2024) and DG-Gen (Hosseini et al.) 2024) focus solely on generating node or edge representation features, but lack the capability to handle textual contents in DyTAGs. As far as we know, while GAG (Ji et al., (025) is the only existing baseline that incorporates textual attributes, it is specifically designed for bipartite social graph generation and lacks generalizability to diverse DyTAG structures and domains. This gap motivates the development of a more universal framework tailored for DyTAG generation tasks.
Given the text-rich nature of DyTAGs, we propose GAG-General, an LLM-based multi-agen agent framework designed for DyTAG generation tasks, building upon the implementation of GAG (Ti et al.) (025). Leveraging the text understanding and generation capabilities of LLMs, framework extends the original GAG with three key enhancements: 1) Generalization: Support both bipartite and non-bipartite graph structures universally. 2) Multi-domain Compatibility: Abstract the DyTAG generation pipeline to enable
GAG-General.
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seamless adaptation across diverse interaction scenarios without domain-specific customization. 3) Standard- ization: Establish standardized task formulations for TDGG and IDGG and incorporate holistic evaluation metrics, ensuring reproducible DyTAG generation. See more details of GAG-General in Section D.I
Framework Details. Following GAG (Ji et al. 2025), GAG-General employs carefully designed LLM-based agents for source and destination nodes, with each node agent maintaining a memory module to record historical neighbor interactions. This memory module effectively captures structural and temporal dynamics from the DyTAG by preserving contextual information about past interactions. We further incorporate an optional memory reflection mechanism, which leverages LLMs distill the node memories into valuable summaries, akin to the message aggregation process in GNNs (Kipl & Welling 2016 Peng et al. 2024). For TDGG and IDGG, GAG-Generaa implements iterative, expansive Dy TYG generation pipeline: in each iteration, the source node agent selects destination nodes based on its memory and contextual features. In the case of IDGG, new nodes are first generated and then updated the node set before this selection step, ensuring the DyTAG's inductive expansion. Subsequently, edges are generated between the selected node pairs. This iterative process continues until the final Dy DyTAG is produced, seamlessly integrating node/edge generation and DyTAG evolution. See more details and pscudocode regarding the implementation of TDGG and IDGG based on GAG-General in SectionD.2
5 EXPERIMENT
5.1 EXPERIMENTAL SETTINGS
Baselines. 1) As our proposed GAG-GGneral an L.LM-based multi-agent generative framework, for the DyTAG generation in TDGG and IDGG, we benchmark three open-source L.LMs: DeepSeek-R1-DWe 1-Distill- Owe (DeepSeek-Al) 2025) (referred to as DeepSeck), Llama-3-70B 7ontt----7-- (AI Meta 2024) (Llama), and Qwen2.5-72B --2en2.5-72BBnnstruue (Team] 2024) (Qwen), as well as closed-source model: GPT-4o-Mini (OpenAl 2024) (GPT), 2) For TDGG, which integrates traditional discriminative tasks from DGNNs in a generative paradigm, compare against five state-of- of-the--rt baselines, including JODIE (Kumar et al.) 2019). TGN (Rossi et al. 2020), CAWN (Wang et al.) 2021a), GraphMixer (Cong et al.] 2023). and DyGFormer (Yu et al. 2023). Performance is assessed on discriminative tasks such as node retrieval (link prediction) and edge classification to benchmark GAG-General against conventional DGNNs. 3) For IDGG, due to its novelty, the challenge of new node generation, and the text-rich nature of Dy we select two of the latest dynamic graph generation models that support node/edge feature modeling and generation: DG-Gen (Hosseini et al.
2024.. VRDAG (Li al. 2024). and TIGGER-I (Gupta et al. 2022). See more details in Section
Implementation Details. Both TDGG and IDGG share basic settings: the first 1,000 edges and the corresponding nodes serve as the initial seed DyTAG, and we set the edge generation size per round as 50. We employ the metrics introduced in Section 4.2 for evaluating the DyTAG generation quality on TDGG and IDGG, including graph structural metrics, textual quality scores, and a graph embedding baaed indicator for joint semputurallempporal-teteeee assessment. See more implementation details in Section
5.2 TRANSDUCTIVE DYNAMIC GRAPH GENERATION
Results of TDGG. In TDGG, GAG- General generates DyTAGs on GDGB with high structural fidelity, as shown in Table 2 The generated graphs exhibit small deviations from ground-truths-most Degree/Spectra MMDs are below 0.3, and six of eight satisfy the power-law validity criterion, indicating high-quality generation. To assess the role of structural and textual information from historical neighbors, we ablate three configurations: w/o node memory, w/ node memory, and w/ node memory & reflection mechanism. As shown in Tables 3 4 both node memory and the reflection mechanism significantly improve textual quality and graph embedding- based metrics across most LLM backbones, due to effective integration and aggregation of historical interaction information. Moreover, experimentally verify the direct usability of generated
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Table 2: The results Degree MMD, Spectra MMD, DR. a, and power-law validity under TDGG with GPT as the LLM backbone. Full results on the other three LLM backbones are available in Table 18
19 and 20
Dataset
Sephora
Dianping
WikiRevision
WikiLife
IMDB
WeihoTech
WeiboDaily
Cora
Degree MMD1
0.023
0.055
0.108
0.181
0.278
0.243
0.247
0.128
Spectra MMD]
0.011
0.328
0.156
0.223
0.316
0.297
0.493
0.156
D2
0.143
0.041
0.056
0.099
0.135
0.030
0.048
0.049
2.993
2.041
2.204
1.720
2.011
1.845
2.378
Power-law Validity
/
X
V
X
V
Table The results on average textual quality scores under TDGG. M. and R. denote node memory and reflection mechanism, respectively. Full results on each scoring criterion available in Table 21 The
29
LLM (GPT) used in evaluation is independent of the LLM backbone used by GAG-General itself. See
Section H.4 for more details. The best and the runner-up scores highlighted in bold and underlined fonts.
DeepSeek
Llama
Qwen
GPT
M.R.
M.R.
w/ M.
w/
Sephora
4.09
4.10
4.37
4.57
4.58
4.66
4.61
4.64
4.70
4.69
4.69
4.77
Dianping
4.29
4.34
4.41
4.13
4.18
4.46
4.14
4.13
4.43
4.32
4.34
4.71
IMDB
3.65
3.82
3.99
3.97
4.02
4.32
4.10
4.18
4.33
3,91
4.02
4.44
Weibo Tech
3,88
3.89
3.92
4.49
4.56
4.86
4.93
4.91
4.96
4.84
4.88
4.97
Table 4: The results on the graph embedding metric under TDGG. See Table
30 for full results.
DeepSeek
Llama
Qwen
GPT w/ M.
M.R.
M.
w/
Sephora 0.715
0.758
0.679
0.675
0.681
0.648
0.579
0.630
0.740
0.588
0.671
0.654
Dianping
0.637
0.666
0.722
0.368
0.386
0.408
0.408
0.390
0.413
0.351
0.368
0.369
IMDB
0.534
0.586
0.532
0.396
0.482
0.533
0.487
0.493
0.519
0.432
0.420
0.435
Weiho Tech
0.415
0.577
0.501
0.335
0.417
0.420
0.296
0.353
0.364
0.327
0.488
0.698
graphs from TDGG downstream tasks compared to the original graphs; experimental details are provided Scalability analysis for TDGG is provided in Section F.I
in Section F.5
Results of Discriminative Tasks TDGG. For node retrieval, results on Hit@1 and Hit@ 10 are reported
Table 36 for edge classification, in Table 39
Although existing DGNNs employ sophisticated modules to model dynamic interactions and achieve strong performance on discriminative tasks (Yu et al. (023), their
effectiveness diminishes when training only 1,000 edges. Notably, shown in Table 39 GAG-General outperforms DGNNs on most datasets, while gains on node retrieval are less pronounced (Table36) This demonstrates GAG-Generee
superior ability leverage structural, temporal, and textual information in DyTAGs, whereas DGNNs exhibit limited generalization due to their reliance on large-scale training data. These results further confirm GAG-General's 's effectiveness in modeling DyTAGs, particularly in capturing dependencies across both discriminative and generative tasks.
5.3 INDUCTIVE DYNAMIC GRAPI GENERATION
Results of IDGG. IDGG, structural quality results are summarized in Table Generated DyTAGs show greater deviation from ground-truths than in TDGG-due to the added complexity of new node
Table 5: The results Degree MMD, Spectra MMD, De, a, and power-law validity under IDGG with GPT as the LLM backbone. Full results on the other three LLM backbones are available in Table 40 and
Dataset
Sephora
Dianping
WikiRevision
WikiLife
IMDB
WeiboTech
WeiboDaily
Cora
Degree MMD]
0.454
0.300
0.267
0.083
0.321
0.268
0.268
0.136
Spectra MMD.
0.189
0.453
0.229
0.208
0.423
0.201
0.439
0.244
0.147
0.069
0.045
0.156
0.091
2.057
2.430
2.050
2.204
1.746
1.867
1.732
2.250
Power-law Validity
X
8

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Published as a conference paper ICLR 2026
Table 6: The results on average textual quality scores under IDGG. Full results are available in Table 43 51
DeepSeek w/ M.
Llama w/ M.
Qwen w/ M.
GPT
w/o M.
w/ M.R.
w/o M.
w/ M.R.
w/o M.
w/ M.R.
w/o M.
w/ M.
w/ M.R.
Sephora
4.63
4.73
4.77
4.58
4.65
4.74
4.58
4.68
4.78
4.58
4.77
4.87
Dianping
4.29
4.44
4.65
4.29
4.51
4.87
4.04
4.50
4.68
4.56
4.71
4.86
IMDB
4.13
4.28
4.39
4.22
4.31
4.43
4.23
4.36
4.51
4.19
4.29
4.49
Weih
4.60
4.75
4.85
3.94
4.00
4.75
4.56
4.74
4.93
4.60
4.71
4.93
Table 7: The results the graph embedding metric under IDGG. See Table 52 for full results.
DeepSeck M.
Llama
Qwen w/ M.
GPT
w/o M.
M.R.
w/o M.
w/
w/ M.R.
w/o M.
w/ M.R.
w/o M.
w/ M.
w/ M.R.
Sephora
0.621
0.661
0.634
0.602
0.612
0.647
0.569
0.587
0.616
0.601
0.603
0.628
Dianping
0.739
0.749
0.769
0.426
0.457
0.512
0.531
0.542
0.578
0.521
0.527
0.505
IMDB
0.601
0.616
0.729
0.511
0.522
0.568
0.502
0.557
0.588
0.521
0.535
0.563
Weibo
0.604
0.591
0,562
0.601
0.629
0.695
0.531
0.547
0.549
0.501
0.514
0.536
generation-yee retain key structural properties. For example, most Degree/Spectra MMDs exceed 0.2, but five of eight graphs still satisfy the power-law criterion, indicating reasonable fidelity. Similar to TDGG, ablation studies (Tables [6] and (7) confirm that node memory and reflection enhance textual and structural quality, underscoring the importance of summarizing historical neighbor information. Moreover,
Table g compares our framework with existing dynamic graph generation models, revealing their significant limitations: generated graphs exhibit notably lower structural fidelity and poorer node/edge attribute richness. These results highlight the need for dedicated DyTAG generation methods. Additionally, we demonstrate that generated graphs from IDGG can
as data augmentation enhance model performance on inductive new nodes; see details in Section F.12 Scalability analysis for IDGG is also included in Section F.T
Hub Node Analysis in IDGG. The generated DyTAGs in IDGG demonstrate critical evolutionary pattern: while maintaining structural congruence with ground-truth graphs, they develop highly connected hub nodes with divergent textual attributes. Specifically, for generating DyTAG with 2,000 edges on Sephora, the top three newly generated hub nodes in the generated DYTAG include products like Vitamin C Serum, Eye Cream, and Neck Cream. In contrast, the ground truth graph's top three hub nodes consist of products like Acne Control Clarifying Cleanser, Rosebud Perfume, and Moisturizing Lotion. The visualization of the hub node structures in the ground-truth and generated graphs shown in Figures 10 and [T This divergence arises because IDGG mimics real-world graph evolution dynamics, ensuring structural fidelity while enabling the creation of new nodes with reasonable attributes that align with the underlying generative patterns of the real world. For instance, in recommendation systems, the hub nodes in the generated Dy can represent emerging products with high potential for virality, while those in the ground-truite graph correspond to established bestsellers. This capability positions DyTAG generation making in e-commerce and digital marketing. By identifying potential future hubs, platforms can prioritize resource allocation for product promotion, optimize advertising strategies, and anticipate market trends before they manifest in real-world data.
strategic tool for proactive decision-
Table 8: The results on the graph structural and the graph embedding metrics under IDGG of our framework and current feature-supportive dynamic graph generation models. Ours correspond to the best results of proposed GAG-General among four LLM backbones. See Table 53 for full results.
Sephoru VRDAG
Dianping VRDAG
Cora
Ouns
DG-Gen
TIGGER-I
Ours
DG--Gin
TIGGERI
Ours
VRDAG
DG-Gee
TIGGER-I
Degree MMD I.
0.370
0.795
0.422
0.622
0.150
0.887
0.167
0.446
0,073
0.877
0.212
0.372
Spectra MMD
0.189
0.847
0,274
0.687
0.351
0,508
0.245
0.341
0.181
0.760
0.365
0.389
Power-law Validity
X
X
Graph Embedding
0.661
0.011
0.228
0.085
0.769
0.024
0,517
0,580
0.828
0:053
0.056
0.197

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Published as a conference paper ICLR 2026
6 FUTURE WORK
Generative Framework Optimization. Leveraging our high-quality, text-rich GDGB datasets, the proposed generative framework achieves groundbreaking performance in the novel DyTAG generation tasks of TDGG and IDGG. Despite this progress, key aspects of the Future work should focus on improving node generation strategies and selection mechanisms, such incorpo- rating adaptive node sampling to balance diversity and fidelity, and leveraging existing DGNN techniques to enhance the accuracy and efficiency of candidate node retrieval and ranking. These advancements, grounded in GDGB, will strengthen DYTAG models and provide a solid foundation for generative graph foundation models, addressing challenges in both structural fidelity and attribute richness.
in IDGG require further refinement.
Further Applications. Beyond model optimization, DyTAG generation holds substantial practical value. As shown in Section [..3] identifying generated future hub nodes- potential hit products in commerce- commerce-demonseee demonstrates its utility in node-level predictive modeling. At the edge and graph levels, DyTAG generation serves effective data augmentation tools for sparse graphs, producing synthetic graphs that preserve structure while enriching textual content. This capability extends applications to e-commerce (modeling generative recommendation), social networks (forecasting misinformation spread), and urban planning (predicting infrastructure usage). By integrating dynamic graph analysis with forward- textual modeling, DyTAG generation can drive impactful real-world solutions across industries.
7 CONCLUSION
In this work, we propose Generative DyTAG Benchmark (GDGB), which resolves existing limitations in datasets, task formulations, and evaluation metrics for DyTAG generation. GDGB comprises meticulously curated datasets with semantically rich node/edge attributes, enabling rigorous evaluation of DyTAG gen- eration. By defining two novel
(transductive generation with fixed node sets) and IDGG (inductive generation with new node generation) effectively model the dynamic expansion of real-world graphs. Additionally, we propose GAG-General, an LLM-based multi-agent framework that generalizes across diverse DyTAG structures and integrates multifaceted metries for holistic evaluation in DyTAG genera- tion. Experimental results demonstrate that GDGB establishes robust benchmarks for DyTAG generation, revealing the interplay between structural and textual features cornerstone for effective generative models. In summary, GDGB lays the groundwork for future research on advancing Dy TyAG generation.
ACKNOWLEDGMENTS
The work was partially done at Gaoling School of Artificial Intelligence, Beijjig Key Laboratory of Research on Large Models and Intelligent Governance, Engincering Research Center of Next-Generatio Intelligent Search and Recommendation, MOE, and Pazhou Laboratory (Huangpu), Guangzhou, Guangdong 510555, China. This research was supported in part by National Natural Science Foundation of China (No. 92470128, No. U2241212), by Beijing Outstanding Young Scientist Program No. 200UWW/Y00020090000000 the National Key Research and Development Plan of China (2023 YFB4502305) and Ant Group through CCF-Ant Research Fund. We also wish to acknowledge the support provided by the fund for building world-class universities (disciplines) of Remmin University of China. by Engineering Research Center of Next- Generation Intelligent Search and Recommendation, Ministry of Education, by Intelligent Social Governance Interdisciplinary Platform, Major Innovation & Planning Interdisciplinary Platform for the "Double-Fine Class" Initiative, Public Policy and Decision-making Research Lab, and Public Computing Cloud, Renmin University of China.
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Appendix: Contents
16
Ethics Statement
18
B
Reproducibility Statement
18
Dafaset
18
Dataset Details
18
Dataset Licenses
20
C.3
Dataset Analysis
21
Comparison with Existing Datasets for Graph Generation.
21
C.5
Comparison Between GDGB and DTGB datasets
22
D
Details of Tasks and Metries
26
D.I Details of GAG-General
26
D22 Details of TDGG and IDGG GAG-Genera]
28
D.3 Details of Metrics
30
Supplementary Experimental Details
32
Large Language Model Baselines
32
E.2
Dynamic Graph Neural Network Baselines
32
E.3
Dynamic Graph Generation Model Baselines
33
E.4
Implementation Details
33
F
Supplementary Experimental Results
34
Scalability Analysis
34
F2
Results of TDGG on Graph Structural Metrics
35
F3
Results of TDGG Textual Quality Metries
36
F.4
Results of TDGG Graph Embedding Metrics
38
F.5
Results of TDGG on Direct Usability Downstream Tasks
39
F.6
Results of Node Retrieval
39
F7
Results of Edge Classification
42
F8
Results of IDGG on Graph Structural Metrics
44
F9
Results of IDGG on Textual Quality Metrics
45
1.10
Results of IDGG Graph Embedding Metrics
47
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E11
Results of VRDAG, DG-Gen, and TIGGER-I
48
F:12
Results of IDGG on Utility in Data Augmentation for Inductive Learning
49
F:13
Visualizations of the Hub Node Structures
49
F.
14
Semantic-drieg Analysis for IDGG Evaluation
49
F.15
Human- aamenty Check for IDGG Evaluation
50
Prompts
52
G.1
Prompt Templates of Bipartite Graphs
52
G22
Prompt Templates of Non- Honaritat Graphs
56
Prompt Templates of
Textual Quality Metrics
59
Use of Large Language Models (LLMs)
63
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A ETHICS STATEMENT
Our proposed GDGB offers both potential benefits and challenges. Positively, it advances DyTAG generation research, enabling applications e-commerce (e.g.. personalized recommendations), social networks (e.g.. community governance), and urban planning frameworks. These advancements could enhance data-driven decision-making while promoting DyTAG generation development through standardized evaluation protocols. Negatively, the misuse of generated DyTAGs might risk generating misleading information or amplifying biases dynamic network scenarios (e.g. illegal user interactions). However, these risks are mitigated under proper usage and regulatory frameworks such as data anonymization, access control, and ethical guidelines- which ensure responsible deployment in real-world applications. By adhering to these principles, GDGB's technical contributions can safely drive innovation in DyTAG learning.
B REPRODUCIBILITY STATEMENT
To ensure the reproducibility of our work, we have made the following efforts: Our pro- posed GDGB comprises eight carefully selected and rigorously processed DyTAG datasets, hosted through
sharing link (attps://ww// www.://wwwwaga...eeee/./.ee com. The proposed GDGB bench- mark and our generative framework GAG-General are fully described in this paper, including detailed dataset construction processes and model implementation specifications. All code and data preprocessing scripts are publicly available in code repository /attps://githu: includes comprehensive instructions for reproducing the TDGG and IDGG experiments. The repository also provides evaluation scripts of our proposed multifaceted metrics facilitate verification of the reported results. The GDGB website is hstttps: gdgb- alo gi github. io/ The website allows researchers to submit methods and track performance via leaderboards on TDGG and IDGG.
gddb--a-benchmark wawteaaaa--ooo-eeeeeeeeeeeeeeeee -ow-eeeeeeerratteedde---------e- learning)......
com/
which
C DATASET
C.I DATASET DETAILS
Sephora is a dataset collected from Kaggle, documenting user reviews of beauty and skincare products on the Sephora e-commerce platform. The temporal span of the dataset ranges from August 28, 2008, to March 21, 2023. Specifically, the dataset includes rich textual information about users, such as skin tone, skin type, hair color, eye color, and historical review statistics. Notably, also contains detailed textual features of beauty products from the Sephora online store, including product and brand names, prices, ingredients, ratings, and all associated attributes, which support the construction of textual features for nodes in this DyTAG. For user reviews of beauty and skincare products, the dataset provides review ratings (ranging from 1 to 5) as edge labels, and detailed textual reviews as edge content. Consequently, the Sephora dataset is represented bipartite DyTAG, where users and beauty products serve nodes with textual features, and an edge represents a user's 's rating and textual review of product at given time.
Dianping Zhang et al. 2013 2014) is a business review dataset derived from Dianping. a prominent platform for business recommendation (e.g., restaurants), spanning from July 3, 2009, to February 8, 2012. The dataset records Dianping users' historical review statistics, frequently reviewed cities, and detailed business information, including store names, addresses, cuisine styles, average costs, and historical scores. User reviews of businesses include multi-dimensional ratings (e.g.. flavor, environment, service) in addition overall scores (ranging from 0 to 5) as edge labels. Reviews also contain rich textual content and supplementary http: http://wwwaaainn......
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evaluations for specific items such as recommended dishes or special services, providing extensive edge textual features. The dataset is structured as bipartite DyTAG, where Dianping users and businesses are nodes with textual features, and an edge represents a user's detailed textual review of business at a given time.
WikiRevisior" a dataset cleaned and processed from Wikipedia's official data dumps, recording user revisions and modifications to Wikipedia pages with the selected dumps corresponding to December 1, 2024. The temporal span ranges from January 30, 2001, to December 3, 2024. The dataset includes Wikipedia users' usernames, historical editing statistics, frequently edited pages, and all revised Wikipedia page titles. To enrich textual features for page nodes, we crawl the first paragraph of each corresponding Wikipedia page from Wikipedia. User revisions are categorized into two edge classes (minor vs. non-minor edits), with the accompanying revision comments serving as edge textual features. The dataset is structured as a bipartite DyTAG, where Wikipedia users and pages are nodes with textual features, and an edge represents user's textual revision summary on a page at given time.
Wikilife (Zhang et al., (022cc is dataset derived from Wikipedia's official data dumps, recording historical records of notable individuals being physically present at specific locations. The temporal span ranges from 202 to 2024. The original dataset (Zhang et al. 2024c) is extracted from English Wikipedia's 's biography pages as person-time-locatio triplets. We extend this by crawling the first paragraphs of corresponding Wikipedia pages for persons and locations as extra textual features. Additionally, we utilize 24 life trajectory activity categories (e.g., birth/death, education, career) from (Zhang et al. (024c) as edge labels and map the original Wikipedia text of triplets to describe the specific activities of individuals at locations. The WikiLife dataset structured as bipartite DyTAG, where persons and locations are nodes with textual features, and an edge represents a person's 's textual life trajectory activity in a location at given time.
IMDE is dataset based on IMDB's ' official data, documenting actor/actress collaboration networks. The temporal span ranges from 1988 to 2031 (including collaboration information of official future films like Avatar 3). The dataset includes actors' and actresses' names, birth/death years, primary professions, and extra biographical information crawled from Wikipedia to enrich node textual features. Edge categories correspond 20 types of movie genres (e.g., comedy, drama, crime, action), with edge texts comprising the title of the collaborated movie and roles played by actors/actresses, respectively. The IMDB dataset is a non-bipartite DyTAG, where actors/actresses are nodes with textual features, and an edge represents movie collaboration relationship between them at a given time.
Weibo (ehhZZang al., 2024b) is a dataset collected from the Weibo social platform, recording user interactions (comments and reposts). The temporal span ranges from December 29, 2023, to January 5, 2024. The original dataset (Zhang et al. 2024b) focuses on advertising strategies for electric toothbrushes. We reprocess the raw data to extract temporal information and restructure it into the DyTAG format. The dataset includes user profiles (e.g., usernames, gender, regions, follower/followee counts, self-introductions)) and interaction edges categorized as comment or repost, with edge texts comprising source post content and destination user comments/repooss. Named WeiboTechh due to its focus on technology-relatee topics (e.g., electronics, automobiles), the dataset features, and an edge represents user interactions at a given time.
bipartit DyTAG, where Weibo users are nodes with textual
WeiboDaily ZZhngg et al. 2024b) is also dataset collected from the Weibo social platform, recording user interactions (comments and reposts). Different from WeiboTeehh the temporal span ranges from December I, 2023, to December 31, 2023, and the original dataset (Zhang al.) (024b) focuses on advertising strategies for ABCReading. Thus, Weibolaill spans a full month and targets daily life topics (e.g.. lifestyle sharing), enabling continuous modeling analysis. Similarly, the dataset includes user profiles (e.g., usernames, gender, regions, follower/followee counts, self-introductiions) and interaction edges categorized as comment or repost,
https://datasets. indbws.comm
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with edge texts comprising source post content and destination user comments/reposts. The dataset is a non- bipartite DyTAG, where Weibo users are nodes with textual features, and an edge represents user interactions a given time.
Cora is an extended version of the classic citation network dataset Cora (Sen et al. 2008). documenting academic paper citation relationships. The temporal span ranges from February 1, 1985, to September 1, 2024. The original Cora dataset records citation relationships and node categories (Sen et al. 2008). We expand the dataset by crawling first-order and second-order paper information via references, enriching the size of the citation network. Node textual features include paper titles, abstracts, authors, and citation counts. Edge texts are extracted from the exact sentence in the citing paper that references the cited paper. With regards the edge labels, based on the section name of each citation in the citing paper, they are mapped to five categories: 1) Intro & Background, 2) Tech & Methodology, 3) Experiment & Conclusion, 4) Topic-specific and 5) Others. The Cora dataset is non-bipartite DyTAG, where papers nodes with textual features, and an edge indicates a citation relationship at a given time.
C.2 DATASET LICENSES
In this section, we provide dataset licenses for each proposed DyTAG.
Sephora: CC BY 4.0 license (Creative Commons Attribution 4.0 International License). The original dataset can be found here
Dianping: CC BY-SA license (Creative Commons Attribution-ShareAlike License). The original dataset be found here
WikiRevision:
GFDL (GNU Free Documentation License) and CC BY-SA license (Creative Commons Attribution-SharAAlik License). The original dataset can be found here
WikiLife: GFDL (GNU Free Documeenation License) and CC BYSSA license (Creative Commons Attribution- ShareAlike License). The original dataset can be found here
IMDB: Subject your compliance with these Conditions of Use and your payment of any applicable fees, IMDB or its content providers grants you a limited, non-exclusive, non-transferable, non-sublicenseable license to access and make personal and non-commercial use of the IMDB Services, including digital content available through the IMDB Services, and not to download (other than page caching) or modify this site, or any portion of it, except with express written consent of IMDB. Additional license terms may be found in the Terms. The IMDB Services or any portion of such services may not be reproduced, duplicated, copied, sold, resold, visited, or otherwise exploited for any commercial purpose without the express written consent of IMDB. This license does not include any resale or commercial use of any IMDB Service or its contents or any derivative use of this site or its contents. All licenses are non-excluee non-exclusive, and all rights not expressly granted to you in these Conditions of Use or any applicable Terms are reserved and retained by IMDB its licensors, suppliers, publishers, rightsholders, or other content providers. You will use all IMDB Services in compliance with all applicable laws. The original dataset can be found here
WeiboTech: CC-BY 4.0 license (Creative Commons Attribution 4.0 International License). The original
dataset can be found here)
WeiboDaily: CC-BY 4.0 license (Creative Commons Attribution 4.0 International License). The original dataset can be found here
Cora: CC BY-SA license (Creative Commons Attribationn-harrAAllke)) The original dataset can be found here
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C.3 DATASET ANALYSIS
To provide a comprehensive overview of our GDGB datasets, we present the node degree distribution in Figure 3 Most datasets exhibit a long-tailed distribution, which aligns with the real-world graph growth patterns governed by power-law principles (Clauset et al., 2009). This characteristic reflects the uneven connectivity observed in natural networks, such as social networks or citation graphs, where a few nodes dominate high degrees while the majority remain sparsely connected.
Furthermore, Figure illlustrates the edge label distribution of the GDGB datasets. The results reveal that most datasets maintain a balanced label distribution, mitigating the risk of performance degradation in edge classification tasks caused by extreme class imbalances. However, a subset of datasets exhibits relatively skewed label distributions, intentionally introducing realistic challenges akin to practical applications (e.g-- rare but critical edge types in life trajectory or movie collaboration scenarios). This dual distribution pattern ensures both diversity and practical relevance, enhancing the dataset's utility for robust model evaluation.
Sephora
Dianping
WikiRevision
10a
10a
10a
10a
10a
10a
10a
10a
104
10a
10a
10
10a
10a
Requency
10a
10a
10a
10"
10
10
10a
10
16
10
10a
WeiboTech
WeiboDaily
10
10a
10'
10
10a
10
10a
104
10a
104
10t
101
10a
10a
100
10a
10a
162
10a
10a
10a
10>
10a
10a
102
Node Degree
Figure 3: Distribution of node degree on GDGB datasets.
C.4 COMPARISON WITH EXISTING DATASETS FOR GRAPH GENERATION.
In graph generation research, while significant progress has been made in molecular graph generation, non- molecular graph generation has received comparatively less attention Faez et al.) 2021). Consequently, existing models for non non-molecular graph generation are often trained on synthetic datasets such as Grid (Dai et al., 2020). Community, and SBM (You et al. 2018 Guo & Zhao 2023), or static, topology-only graphs, such as Ego and Polblogs (Chen et |l.] 2023). Further, we observe lack of focus on dynamic graph generation, despite the fact that real-world non- mole-mll graphs inherently exhibit temporal evolution in both topology and node attributes (Guo & Zhao 2023), Among the most commonly used continuous
dynamic graph datasets, such as LastFM and MOOC (Hosseini et al. 2024 Gupta et al. 2022 Poursafaei jet al., 2022b). both are derived from interaction networks (Poursafaei et al. 2022a) that inherently include textual attributes of users/items and timestamped interactions. However, these datasets suffer from critical limitations: MOOC provides only numerical features extracted from raw text, while I LastEM discards attribute information entirely.
Although widely adopted, existing datasets face notable challenges in modeling the co-evolution of temporal, structural, and textual attributes in graphs. First, static graph datasets lack temporal annotations, making
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Sephora
Dianping
10
10a
106
10a
10
10*
10
Frequency
100
10a
10a
100
2
10
IMDB
WelboTech
WelboDaily
Cora
10c
10a
10a.
10a
10a
10*
10a
10"
102
10
10a
10a
Edge Label
Figure Distribution the number of edges for each label on GDGB datasets.
them incompatible with dynamic graph generation tasks. Even discrete-time dynamic graph datasets like Bitcoin-Alpha (Kumar et al.] 2016 Gupta et al., 2022), which usually represent graphs as discrete daily snapshots, suffer from sparse temporal distributions of edges, complicating the fine-grained modeling of structural and temporal dynamics. Second, most dynamic graph datasets omit raw textual data, relying instead on preprocessed numeric or categorical features (Hosseini al., 2024), which limits the exploration of rich textual attributes in real-world scenarios. Finally, more recently, datasets such as the Dy DyTAGs from DTGB (Zhang et al. 2024a). while providing edge-level text features, often lack sufficient and high-quality node-level textual information. For instance, six out of eight datasets of DTGB contain (source) node texts that are typically just identifiers like email addresses or username. This is critical limitation, as real-world applications frequently require detailed node-wise text attributes to disambiguate entities beyond IDs. The statistics the above mentioned datasets and our proposed datasets are shown in Table 9
C.5 COMPARISON BETWEEN GDGB AND DTGB DATASETS
We provide comprehensive analysis and empirical comparison between our proposed GDGB and the existing DTGB. While both benchmarks contain dynamic text-attributed graphs, we demonstrate that GDGB is fundamentally distinct in construction philosophy, data quality, and utility for generative tasks.
Textual Information Richness and Functional Utility. As illustrated in Figure 5 we compare the average text length of node and edge attributes across datasets GDGB and DTGB, demonstrating that GDGB datasets exhibit significantly richer textual content. However, text length alone does not guarantee utility in generative modeling. To assess the functional value of textual features, we evaluate two feature-aware dynamic graph generation models-VRDAG (Li et al.) 2024) and DG-Gen (Hosseini et al.) 2024) both benchmarks, with and without the incorporation Figures and 7 textual features consistently improve generation quality GDGB, as evidenced by reduced Degree and Spectra MMD scores. In contrast, on DTGB, the inclusion of textual features often degrades performance-parricullayy for VRDAG, which is highly dependent node feature inputs. This suggests that the textual attributes in DTGB are semantically impoverished or noisy, and thus detrimental to generative tasks, whereas GDGB's s textual content is both meaningful and effectively leveraged by learning models.
BERT-based textual embeddings. As shown
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Table 9: Statistics of our proposed datasets and comparison with existing datasets for generative tasks. |V Vlmax and E max represent the maximum number of nodes and maximum number of edges in the graph list of the dataset, respectively.
Dataset
Nodes
Edges
Fdge Categories
Timestamps
Domain
Text Attributes
Bipartile
Previous Static Gruphs
Community
Grid
V lax
V
361
160
E maa
1145
694
Synthetic
Synthetic
max
max
Social Network
Polblogs
1,222
16,714
Interaction
MOOC
7,047/97
411.749
345,600
Interaction
Previous
LastFM
980011000
1,293, 103
1,283,61
Interaction
Dynamic
Reddit
10,000/984
672.447
669.065
Social Interaation
Gruphs
Wikipedia
157.474
152,757
Bitcoin-Alpha
3,783
24,186
191
Financial Network
Stack elec
67. 157755330_544
1,262,225
2
5.224
Multi round dialogue
Nods & Edge
Stack ubantu
180 9902261/193,987
1,497,006
1,972
Mulli-round dialogue
Nod: & Edge
DyTAGs
Previous
(DTCB)
Amazon movies
Googlemap CT
Yelp
I. 196.88961550,346
233,4596001007
83.796/27
6,99 6,990,189
3,217,524
5
6,036
7,287
E-commerce
E-commerce
E-commence
Nade & Edge
Node & Edge
Edge
Enron
42,711
797.907
Node Edge
GDRET
6,786
1,339,345
2,591
Knowledge graph
ICEWS1819
31,796
1,100,071
266
730
Knowledge graph
Node & Edge
Sephora
210.357/2.274
801,234
5
5,314
E. commerce
Node & Edge
Disnping
158, 158,541/88.11
1,990.409
748,151
E-commerce
Node Edge
75.622/3.204
2,778.732
2,766,153
Edge
GDGB
Wikat life
IMDB
406, 148/54,513
125,714
1,534,162
1,996,520
24
20
1,810
122
Movic Collibrrtionn
Celebrity Ringmphy
Nade & Fdge
Nod: & Edge
WeiboTech
20,767
79.925
Network
Node Edge
WeiboDaily
66,500
354.098
293,662
Social Netweck
Nods & Edge
Corn
48,797
110,788
8,274
Citation
Node & Edge
Sephora
CDCB D3
Dianping
WikiRevision.
Wikllife
IMDB
Weibo Tech
WeiboDally
Dalaset
Cora
Datasd
Enron 21.7
Gonglemap CT
Amazon movies
GDELT a12.8
28.5
ICEWS1819
330.0
Stack ubuntu
Yelp
10a
10a
10a
10a
Aug Node Teex Longth (log scale)
Avg Edge Text Length (log scale)
Figure 5: Left: Average node text lengths on GDGB and DTGB datasets. In non-bipartite graphs, text lengths are averaged across all nodes. For bipartite graphs, averages calculated for source nodes. Right: Average edge text lengths on each dataset.
Intrinsic Text Quality Evaluation. To directly evaluate the linguistic quality of node and edge texts, conduct intrinsic analysis using two complementary metrics: (i) Perplexity (PPL), where lower values indicate higher fluency and coherence with natural language distributions; and (ii) LLM-based human- like scoring (on a 1-5 scale) across five dimensions: Contextual Fidelity, Personality Depth, Dynamic Adaptability, Immersive Quality, and Content Richness. The aggregated results summarized in Table TO
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Lase mis
Sephora
-2..%
19.1%
3301%
30.0%
GDGB "ext
Dianping
-34.7%
-18.8%
-24.4%
-33.2%
pTca sgn Text
-13.3%
-18.9%
-1.68
DTS8 Text
WikiLife
-12.0%
-5.9%
-22.5%
-28.8%
IMD8
-15.8%
WeiboTech
-14.0%
-15.2%
-20.5%
-20.1%
Datasse setaset
WelboDally Ry
-11.7%
Detasct
Cora
-12.1%
Data
-17.2%
-19.1%
-11.5%
Enron
+3.4%
+10.9%
Googlemap CT
-20.2%
-14.9%
-0.4%
+4.3%
Amazon movies
-17.4%
+3.0%
-4.0%
GDELT
+6.1%
+0..5%
+5.88%
+2.6%
ICEWS1819
0%
-15.2%
-1.2%
+0.1%
+00.45
+15.0%
+11.4%
Stack ubuntu
+3.2%
+-8.7%
+3.5%
+1.4%
-32.5%
-25.6%
-19.3%
-17,5%
0.6
Spectra MMD
Degree MMD
Spectra MMD
Figure 6: Left: Comparison of Degree MMD w/ and w/o text on GDGB datasets and DTGB datasets in DG-Gen. Right: Comparison of Spectra MMDD w/ and w/o text on GDGB datasets and DTGB datasets in DG-Gen.
Figure 7: Left: Comparison of Degree MMD w/ and
w/o text on GDGB datasets and DTGB datasets in VRDAG. Right: Comparison of Spectra MMD w/ and w/o text on GDGB datasets and DTGB datasets in VRDAG.
GDGB exhibits significantly lower perplexity (average value less than 90), indicating that its textual content is more coherent and aligned with natural language patterns. Furthermore, LLM evaluations confirm the superior semantic quality of GDGB, with average scores exceeding 4.0 across both node and edge texts. In contrast, DTGB achieves notably lower scores for node texts (average: 3.03), particularly in datasets such as Enron, where node attributes often consist of identifiers (e.g., email addresses) lacking meaningful semantics.
Table 10: Intrinsic text quality comparison (averaged across datasets).
Metric
GDGB DTGB
PPL of node texts J
193.58
75.95
PPL of edge texts
181.22
88.17
LLM rating (node texts)
3.03
4.21
LLM rating (edge texts)
3.55
4.17
Generative Performance on GAG-General. To further assess the suitability of each benchmark for DyTAG generation, we apply the newly proposed GAG-General framework all datasets from DTGB and all datasets from GDGB, using GPT as the LLM backbone under both TDGG and IDGG tasks. All models are evaluated under identical configurations in DecctionE.44 to ensure fair comparison. The results under TDGG
are presented in Table 11 12
and [3 While, the results under IDGG are presented in Table [5] T5] and [6]
The above results reveal consistent advantages for GDGB across structural, textual, and embedding fidelity metrics. For instance, in the graph embedding metric under TDGG (Table 13). DTGB results predominantly fall within the 0.2-0.4 range, while GDGB achieves substantially greater similarity (0.4-0.7), indicating
better preservation of global structural and semantic properties. An even more pronounced trend is observed under IDGG: none of the DyTAGs generated on DTGB datasets satisfy the plwer-law validity criterion (Table 14), and both average textual quality scores (Table 15) and graph embedding metrics (Table T6) generally lower than those achieved on GDGB datasets. This again highlights the limitations of DTGB' s textual content in effectively supporting generative Dy DyTAG tasks. Although the integration of memory and
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reflection mechanisms in GAG-General yields improvements on DTGB, these gains are constrained by the underlying data limitations, underscoring the dataset's inherent constraints for generative tasks.
Table 11: The results of graph structural quality under TDGG with GPT as the LLM backbone.
Renchmark
Dataset
Degree MMD
Spectra MMD
D2
Power-law Validity
Enron
0.331
0.266
0.125
1.982
x
GDELT
0.252
0.326
0.174
1.829
x
ICEWS1819
0.302
0.341
0.132
1.794
x
DTGB
Stack clec
0.228
0.345
0.133
1.822
X
Stack ubuntu
0.235
0.359
0.130
1.901
X
Googlemap CT
0.255
0,375
0,080
2.278
Amazon movies
0.404
0,350
0.182
1.636
X
Yelp
0.213
0.224
0.121
2.033
Sephora
0.023
0.011
0.143
2.993
Dianping
0.055
0.328
0.041
2.234
WikiRevision
0.108
0.156
0.056
2.041
Wikil.ife
0.181
0.223
0.099
2.204
GDGB
IMDB
0),778
0.316
0.135
1.720
Weibo
0.243
0.297
0.030
2.011
WeiboDaily
0.247
0.493
0.048
1.845
Cora
0.128
0.156
0.049
2.378
Table 12: The results average textual quality scores under TDGG with GPT the LLM backbone. M. and R. denote node memory and reflection mechanism, respectively.
Benchmark
Dataset
w/o M.
w/ M.
w/ M. & R.
3.65
3.72
3.83
GDELT
3.70
3.98
ICEWS1819
3.47
3.87
4.14
Stack elec
3.68
3.70
3.96
DTGB
Stack ubuntu
3.38
3.56
3.78
Googlemap CT
3.10
3.30
4,06
Amazon movies
3.78
3.79
4.11
Yelp
3.80
4.23
4.33
Sephora
4.69
4.69
4.77
Dianping
4.32
4.34
4.71
WikiRevision
4.74
4.79
4,96
Wikil.ife
4.44
4.46
4,59
GDGB
IMDB
3.91
4.02
4,44
WeiboTech
4.84
4.88
4,97
WeiboDaily
4.80
4.92
4.99
Cora
3.98
4.10
4.52
Summary. Collectively, the experimental results demonstrate that GDGB significantly outperforms DTGB across multiple dimensions of generative evaluation. The superior performance structural metrics, textual quality, and embedding similarity highlights GDGB's enhanced semantic richness and compatibility with generative modeling frameworks. In contrast, the limited and often noisy textual content in DTGB hinders its effectiveness in supporting high-fidelite DyTAG generation. Therefore, GDGB is not merely an extension of DTGB with longer text, but puilt benchmark for generative DyTAG tasks, where high-quality textual attributes are core design principle. These findings establish GDGB as a more suitable foundation for ad advancing research in generative modeling of dynamic text-attributed graphs.
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Table 13: The results on the graph embedding metric under TDGG with GPT as the LLM backbone.
Benchmark Dataset
w/o M.
w/ M.
w/ M. & R.
Enron
0.201
0.259
0.400
GDELT
0.255
0.348
0.393
ICEWS1819
0.198
0.289
0.383
Stack eloc
0.235
0.319
0.291
DTGB
Stack ubuntu
0.223
0.327
0.330
Googlemap CT
0.271
0.302
0.462
Amazon movies
0.303
0.359
0.427
Yelp
0.279
0.396
0.411
Sephora
0.588
0.671
0.654
Dianping
0.351
0.368
0.369
WikiRevision
0.438
0.442
0.454
WikiLife
0.459
0,,553
0,463
GDGB
IMDB
0.432
0.420
0.435
WechoTech
0.327
0.488
0.698
WaiboDaily
0.681
0.689
0.694
Cora
0.511
0.446
0.465
Table 14: The results of graph structural quality under IDGG with GPT as the L.M backbone.
Benchmark
Dataset
Degree MMD
Spectra MMD
D;
Power-law Validity
Enron
0.412
0.378
0.132
1.742
GDELT
0.489
0,456
0.167
1.891
ICEWS1819
0.503
0,412
0.184
1.623
Stack clec
0.537
0,459
0.181
1.654
DTGB
Stack ubuntu
0.521
0,488
0.195
1.577
X
Googlemap CT
0.398
0.342
0.112
1.803
X
Amazon movies
0.476
0.401
0.143
1.668
X
Yelp
0.355
0.317
0.078
1.921
X
Sephora
0.454
0.189
0.147
2.057
Dianping
0.300
0.453
0.069
2.430
WikiRevision
0.267
0.229
0.045
2.050
WikiLife
0.083
0.208
2.204
GDGB
IMDB
0.321
0.423
0.252
X
WeiboTech
0.268
0.201
0.064
1.867
X
WeiboDaily
0.268
0.439
0.156
Cora
0.136
0.244
0.091
2.250
DETAILS OF TASKS AND METRICS
D.1 DETAILS OF GAG-GENERAL.
GAG-General is generalized agent-based framework designed for both TDGG and IDGG within the DyTAG paradigm. It extends the existing GAG framework (Ji et al. 2025) through key architectural and procedural generalizations, enabling broader applicability and domain adaptability. The full procedure is specified in Algorithms and here we detail its design and core distinctions from GAG.
The framework proceeds in three stages. First, generalized node formulation initializes node agents, each optionally equipped with Node Memory and Reflection Mechanism to maintain and summarize interaction history. Unlike GAG, this stage supports both bipartite and non- non-bipartite graph structures, allowing modeling
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Table 15: The results on average textual quality scores under IDGG with GPT as the LLM backbone. M. and R. denote node memory and reflection mechanism, respectively.
Benchmark
Dataset
w/o M.
w/ M.
w/ M. & R.
Enron
3.51
3.89
3,93
GDELT
3.62
3.82
4.02
ICEWS1819
3.59
3.90
4.25
Stack elec
3.23
3.46
3.64
DTGB
Stack ubuntu
3.19
3.47
3.73
Googlemap CT
3.22
3.42
3.87
Amazon movies
3.63
3.75
4.22
Yelp
3.53
4.03
4.16
Sephora
4.58
4.77
4.87
Dianping
4.56
4.71
4,86
WikiRevision
4.39
4.54
4.71
WikiLife
4.28
4.39
4.47
GDGB
IMDB
4.19
4.29
4.49
Weibo Tech
4.60
4.71
4,93
WaiboDaily
4.74
4.83
4,99
Cora
4.27
4.36
4.57
Table 16: The results the graph embedding metric under IDGG with GPT as the LLM backbone.
Benchmark
Dataset
w/o M.
w/ M.
w/ M. & R.
Enron
0.221
0.278
0.387
GDELT
0.242
0.359
0.388
ICEWS1819
0.234
0.297
0.361
Stack elec
0.243
0.289
0.321
DYGG
Stack ubuntu
0.239
0.319
0.309
Googlemap CT
0.289
0.333
0.330
Amazon movies
0.294
0.338
0.396
Yelp
0.281
0.369
0.417
Sephora
0.601
0.603
0.628
Dianping
0.521
0.527
0.505
WikiRevision
0.589
0.614
0.629
WikiLife
0,510
0.534
0,531
GDGB
IMDB
0.521
0.535
0.563
WechoTech
0.501
0.514
0.536
WeiboDaily
0.459
0.481
0.502
Cora
0.541
0.552
0.572
of diverse network types. Second, during abstracted interaction simulation, agents generate interactions (or new nodes in IDGG) over sequential rounds. Guided by an LLM backbone and informed by memory and reflection, agents produce textual content such as relations and attributes. Crucially, this workflow abstracted from domain-specific rules, enabling seamless transfer across datasets without customization-a significant departure from GAG's rigid, task-specific designs. Finally, in the graph projection and evaluation phase, all generated elements compiled into dynamic graph and evaluated using nulti-dimeesional metrics.
Hence, the key generalizations over GAG lie in three aspects. 1) GAG-General removes the bipartite constraint, supporting arbitrary graph topologies. 2) replaces GAG's domain-specific simulation logic with unified, abstracted process, enhancing reusability across domains. 3) While GAG focuses single
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generation mode, GAG-General explicitly supports both TDGG and IDGG, capturing distinct dynamics of temporal evolution and structural growth.
sum, GAG-General serves as demonstration framework that validates the feasibility of generative DyTAG modeling. Its generalizations extend beyond the original GAG's limitations, offering more flexible foundation for future work. While our primary contribution is the GDGB benchmark- ennonpssin datasets, tasks, and metrics Saariis-AAG-GGeeeed illustrates how such benchmarks can drive the development of more generalizable generative frameworks.
D.2 DETAILS OF TDGG AND IDGG ON GAG-GENERAL
Transductive Dynamic Graph Generation. TDGG operates on a fixed node set N, evolving from an initial seed graph Go = (A, to final graph Gx (N, after K rounds. For instance, during the k-th round S k < K). the DyTAG generation process includes the following three stages:
1. Node Update: Based on the given source node M and destination node set A from the last round, first update the node
M - U W and MV _ U. dal where
Mrt and N obtained based on the original DyTAG with pre-defined size. We then initialize active source nodes as Nk active = for the following transducitve generation.
2. Node Selection: According to activeive we activate source node agents to perform pairwise interactions. LLM-based agents recall and select final destination nodes Art dkseslles from Nir by analyzing the source node's textual profile and its memories. The memory reflection mechanism optionally used to summarize the memories.
3. Interaction Generation: After the destination node selection, the active source node agents in the k-th round generate new edge set E (between A/7kacttvve and Autt sslooe, including edge labels Lu,v and edge texts &g,9- We then update the edge E Ex-1 ukk
The destination node selection and edge generation stages in TDGG naturally integrate traditional discrimi- native tasks (e.g., node retrieval, edge classification) with LLM--riven text understanding and generation, forming a DyTAG-specific generative paradigm. The pseudocode of TDGG on GAG-General is shown in Algorithm ]
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Algorithm 1:
Transductive Dynamic Graph Generation
Require: Initial seed DyTAG Go (N, 8 rounds K, edge generation size S
Ensure: Final generated DyTAG 9K = (E
1: Initialize N NCmit ,No dst Ami 0 iiit
2: for = to K do
3: Node Update:
4: N Sample(N, N
dst Sample(N,
5: N - N sec dst = -Nhkl UN dst
Nok
6: NAt VRk-nctive = -
7: Node Selection:
8: Ni
Ark-seles select = Recall, Select N active NNk
sre
dsl
9: Interaction Generation:
10: Eh GenerateEdges (aaeeessNiaaaai active N
11: Ek = - u
12: end for
13:
return GK (N,EK)
Inductive Dynamic Graph Generation. IDGG extends TDGG by simultaneously generating new nodes and edges. Starting from seed graph 9n = (A", ED), evolves to 9x (N* gK) through K rounds. Key steps for the k-th round (1 < k < K) include:
1. Node Generation: We apply node generator agents generate N (size of Rare) and A (size of Raa) along with their textual features, according to the recent active nodes. Race and Ras are predefined by the seed graph based on the average numbers new source nodes and new destination nodes per round.
2. Node Update: After node generation, we update the node sets as A/t
A U A and sec
ste
N/t A W We then initialize active source nodes A RndSample(AA.. for the following inductive generation, where RndSample() is random sampling function.
dst det'
3. Node Selection & Interaction Generation: Similar to TDGG, active source node agents recall and select destination nodes Aik from Ark and generate E (between N and A V/k-select) including edge labels Lu,v and edge texts u,p" We then update the edge set as g ck-1 U U.k
The IDGG above faithfully replicates the evolutionary dynamics of real-world DyTAG through node and edge generation over time. Although inherently challenging, this paradigm establishes foundational direction for advancing dynamic graph generation research by simulating open-ended growth processes. The pseudocode of IDGG on GAG-General is shown Algorithm 2 The source/destination node generation counts / Rag for IDGG shown in Table 17
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Algorithm 2: Inductive Dynamic Graph Generation
Require: Initial seed DyTAG Go
(N", E rounds K, edge generation size S, source/destination node
generation numbers Rase Rast
Ensure: Final generated DyTAG OK (NK ,EK)
1: Ass Ainit /init .Vss Winnt E c3 sre are
det
dst
2: for
to K do
3: Node Generation:
4: Nít GenerateNodes((R ) N GenerateNodes((e. (Rat)
5: Node Update:
6: Nkk -Nk-1 UNK sec Noh Naha UN
7: N RndSample(AVt. sec: S)
8: Node Selection:
9: - seleet Recall, Select(N Select(AVkaadtin active dst
10: Interaction Generation:
11: E GenerateEdgee se0
-kiii ,aaiie,,,aa Nohh solecc))
12: 8h gh-1 U
13: end for
14:
return GK = (NK, (NK
Table 17: The source node generation counts Rere and the destination node generation counts Rast, which are determined by the seed graph.
Dataset
Sephora
Dianping
WikiRevision
WikiLife
IMDB
WeiboTech
WeiboDaily
Cora
Rarc Rast
27
33
17
3
32
4
44
37
12
17
29
19
18
D.3 DETAILS OF METRICS
We provide more detailed mathematical formulations and implementation specifics of our evaluation metrics Section 4.2 as follows.
Graph Structural Metrics. We evaluate the structural quality of generated DyTAGs using two classical approaches:
Degree/Spectra MMD. This metric quantifies the discrepancy between the distribution of graph descriptors (e.g., degree/spectral properties) in the generated and ground-truth graphs (Xiang et al. 2022 You et al. 22088) Chen et al. [202)). We compute the distribution distance, using the maximum mean discrepancy (MMD) with a radial basis function (RBF) kernel. Thus, lower MMD values indicate higher structural fidelity. Specifically, we employ positive definite RBF kernel with smoothing parameter v, defined as:
(- ||0; - x. 11
t (I;, = cxp
2u
where H, and yy are feature vectors representing graph descriptors (c.g., degrees/spectral properties) for nodes and j. denotes smoothing parameter controlling the width of the RBF kernel. Larger u values reduce sensitivity to local variations. k((,,) denotes the RBF kernel function measuring similarity between graph descriptors.
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We treat nodes as samples, where n and m denote the number of nodes in the generated and ground-truth graphs, respectively. The MMD is computed as:
n Σ (I), + MMD* == n 45==1
m
n1
m
k (1),
k (T)
m
VLTTE
where X { D2 ................................ In Jis the set of feature vectors from the generated DyTAG. Y {uh, .2..... , Vm is the set of feature vectors from the ground-truth DyTAG.
Power-law Analysis Real-world networks often exhibit power-law degree distributions. We assess power-law behavior in degree distributions using the Golmogorov-Smirnor (KS) distance:
D
max 12
[F(k) (k)):
where F(k) is the empirical cumulative distribution function (CDF) of the degree data, and H (A) is the CDF of the best-fitting power-law model. Amin denotes the minimum degree value used for fitting, set to kmin 2 to ensure robustness. The power-law exponent a indicates adherence to a power-law distribution, typically valid if [2, 3] (Clauset al., 2009). A DyTAG is power-law valid if D3 < 0.15 and the power-law exponent E [2.3].
Textual Quality Metrics, Inspired by recent role-playing agent research (Wang et al. 2024b Chen et al. 2024 Wang al. (0024)), employ the LLM-as-Evaluane framework to assess the text quality in the generated DyTAGs, Considering the targets and requirements of DyTAG generation tasks, we define five scoring criteria:
1. Contextual Fidelity: Consistency of node/edge texts with historical interactions.
2. Personality Depth: Richness of semantic and stylistic diversity in node profiles.
3. Dynamic Adaptability: Temporal coherence of evolving textual content.
4. Immersive Quality: Engagement and realism generated narratives.
5. Content Richness: Information density and relevance of edge texts.
Each criterion is scored on scale by LLM evaluators, ensuring multidimensional assessment of textual quality. For the LLM-as for all baselines, with hyperparameters (e.g., temperature, top-p. and repetition penalty) kept identical to those specified in Section E.4 The prompts used in the LLM-as-Evaluator framework are provided in Section G.3
LLMMas-Evaluaa Evaluator component, we consistently employ GPT as the underlying LLM backbone
This approach outperforms embedding-
metrics like BERTScore (Zhang et al.) 2020 by avoiding information loss during feature compression.
Graph Embedding Metric. To jointly evaluate structural, temporal, and textual fidelity, we extend the JL-Metric (Hosseini et al.) 2025) to a better adaptation DyTAGs with textual node feature integration to a graph embedding- based indicator. Specifically, the metric is calculated through three stages:
Node Embedding Construction. For node j. we concatenate textual attributes and temporal information of historical interactions into contextualized node embedding:
[ ... [ >[
J.r where T(Tr,) = (Ty Ejr (t.), (fect)) encodes the i-th historical interaction event (including times-
tamp Ty.. textual edge feature (t,), and textual interacted node feature Aftet), and my is the 1 total historical interaction count node j. Additionally, we append the textual attribute of node j as the final component of the embedding vector, denoted by Afext
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Random Projection Mapping. Leveraging the Liinson-Lindensstauus theorem for transforming varying dimensionality with bounded error guarantees (Hosseini et al., 2025), we project high- dimensional embeddings into consistent space with random matrices, which enables cross- grassh alignment despite different node counts and interaction histories. The projected graph embedding is:
= [V1. V...... Vy....
where v, denotes the j-th projected node embedding.
Similarity Measurement. We compute the cosine distance between the generated graph gg and ground-truth graph G.:
G.) || F 110.
p 9o) = 1
where || ||r is the Frobenius norm and (;)) is the Frobenius inner product. Lower p values indicate higher global fidelity.
E SUPPLEMENTARY EXPERIMENTAL DETAILS
E. LARGE LANGUAGE MODEL BASELINES
LLMs possess strong capabilities in language understanding and generation, demonstrating impressive performance across wide range of natural language processing tasks. To comprehensively evaluate the performance of different models, we selected several representative LLMs for comparison in our experiments. These include open-source models: SrepSeck-R1-Distie -ienill-Qwwen-3BBB (DeepSeek-Al 2025), Llama-3-70B Instruct (AI@ Meta 2024), and Qwen2.5-722 Instruct (Team (2024). In addition, we include a closed-source
model, GPT-4o-Mini (OpenAl 2024), reference baseline.
E.2 DYNAMIC GRAPH NEURAL NETWORK BASELINES
JODIE (Kumar et al. 2019). This model is a representation learning framework for nodes in temporal networks that captures dynamic embedding trajectories based on sequences of interactions. JODIE uses two coupled recurrent neural networks to update the states of interacting entities and employs projection operation forecast future embedding trajectories.
TGN (Rossi et al. 2020). This method models sequences of time-stamper events. By combining memory modules with graph-based operators, TGN effectively captures temporal and structural dependencies evolving graphs. This innovative architecture enables TGNs to maintain historical context while updating node representations real time.
CAWN (Wang et al., 2021a). This approach inductively represents temporal networks through an anonymiza- tion strategy based on sampled walks. These walks explore the causal structure of network dynamies and generate inductive node representations. A neural network encodes and aggregates the sampled walks to produce the final node embeddings.
GraphMixer (Cong et al., 2023). This model is simple yet effective architecture for temporal link prediction, composed three key components: a multi-layer perceptron (MLP) link encoder to capture information from temporal links, node encoder using neighbor mean-pooss mean-pooling, and an MLP-based link classifier for prediction.
DyGFormer Yu et al., 2023). This method is Transformer-base model for dynamic graph learning that leverages nodes' historical first-hop interactions to learn meaningful representations. introduces a neighbor co-occurrence encoding scheme to capture correlations between source and destination nodes based
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on their interaction histories. To handle longer sequences efficiently, DyGFormer uses patching technique that divides each historical sequence into smaller segments, allowing the Transformer to process long-term dependencies effectively.
E.3 DYNAMIC GRAPH GENERATION MODEL BASELINES
DG-Gen (Hosseini al. 2024). This model is a generative framework for CTDG that enables assumption- free and inductive graph generation. Built as an encoder-decoder model, it learns conditional probabilities of temporal interactions to model topologiaal evolution effectively. By relying on temporal embeddings instead of node IDs, DG-Gen supports inductive learning, allowing it to generate graphs with unseen nodes, timestamps, and edge features.
VRDAG (Li et al., 2024). This model is novel framework for simultancously generating dynamic graph topology and node attributes in a data-driven manner. It uses a bi-flow graph encoder to preserve directional message flow and attribute information in node embeddings. A GRU-based module captures temporal dependencies, updating hidden node states across time. VRDAG also parameterizes a flexible prior distribution sample latent variables at future timesteps, enabling it to model complex dependencies within and across topology and attribute evolution. This variational approach effectively captures both structural and attribute dynamics evolving graphs.
TIGGER Gupta et al. (2022). This model is scalable generative model for continuous-time dynamic graphs that overcomes the transductive limitations and node identity leakage of prior methods. Its inductive variant, TIGGER-I, learns a distribution over node embeddings rather than node IDs, enabling generation of graphs with unseen nodes. Notably, TIGGER-I employs a GAN-based module to jointly generate realistic features and graph structure, supporting flexible up- down-sampling of graph size without requiring one-to-one node mapping-making it suitable for privacy-sensitive and feature-aware dynamic graph synthesis.
E.4 IMPLEMENTATION DETAILS
Implementation Details of TDGG. For TDGG, we set the size of the target expanded DyTAG as 10,000 edges. To ensure fairness in discriminative task comparisons, we adopt dataset split of 1,000/1,000 10000,,00//..... 0000 (train/val/tstt for DGNNs based on the implementation of DyGLib (Yu et al. 2023 Zhang et al. 2024a). and compare with the last 8,000 edges in the generated DyTAG from GAG-General. For edge classification, we evaluate the models using weighted Precision, Recall, and F1 scores (Zhang et al. 2024a). For the discriminative task comparisons in TDGG, following DTGB (Zhang al. 2024a), nodeledge texts are encoded using the BERT-base-uncased model (Devlin et al. 2019) as initialization for each DGNN. In terms of discriminative task-specific metrics, we use Hit@1 and @ 10 for node retrieval (link prediction) tasks. Specifically, for the node retrieval task, we set the number of negative samples to 100 for each positive sample. Notably, the @it@ metric can be considered more challenging form of link prediction, as involves a Dsstive-to-negativv sample ratio of 1:100, compared to the easier 1: ratio.
Implementation Details of IDGG. For IDGG, we set the size of the target expanded DyTAG as 2,000 edges, due to the additional stage of node generation. To ensure fair comparisons with dynamic graph generation baselines, we use the seed DyTAG with 1,000 edges as the input data for training DG-Gen and VRDAG and adjust the generation ratio in DG-Gen to generate 2,000 edges using its publicly available code (Hosseini jtt al. 2024), whereas VRDAG does not offer explicit generation size control (Li et al., 2024). Therefore, evaluations conducted based on the graphs actually generated by VRDAG. As the current dynamic graph generation models' lack of support for DyTAG's 's textual features, our focus is on comparing the quality of generated graph structures and graph embeddings, rather than the texts. Similar to TDGG, following DTGB (Zhang et al. (0044)), node/edge texts are encoded using the BERT-base-uncee initialization for each dynamic graph generation baseline.
model (Devlin et al. 2019)
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Implementation Details of GAG-General on TDGG and IDGG, Following GAG (Ji et al. 2025), we maintain a memory module for each node in DyTAG to record the information of its historical interacted neighbors, effectively incorporating structural and temporal dynamics from the DyTAG. In our proposed GAG-General, the node memory module is implemented via random walks (Sauerwald & Zanetti 2019). We set the number of random walks for each node as 10, with random walk length of 10. The node memory is capped at maximum memory context length of 1,000. Additionally, our generative framework includes an optional memory reflection mechanism, which leverages the LLMs to distill node memories into valuable summaries, akin to the message aggregation process in GNNs (Kipf & Welling/ 2016 Peng jet al. 2024). During the destination node selection process for the active source node agent, for both TDGG and IDGG, set the number of recalled candidate destination nodes for each source node to 10. For the inference parameter configuration of benchmarked LLMs, we set the temperature as 0.8, the top-p (nucleus sampling) as 0.9, the repetition penalty as I.1, and a maximum token limit 2,000 to balance randomness, diversity, and redundancy suppression. With regarding to the LLM prompt design for TDGG and IDGG, we incorporates detailed node descriptions, edge descriptions, memory reflection instructions, interaction instructions, and node generation instructions in our proposed GAG-Genera The example full templates on Sephroa (bipartite) and WeiboTTech (non-bipartite) are provided in Section G As for the L.LM-s-- -a---aluaatoooo framework of the textual quality metric, based on the implementation of CharacterBox (Wang et al. (024a), we use GPT as the LLM backbone for the evaluation framework. The hyperparameters (temperature, top-p, repetition penalty, etc.) of the LLM used in the textual quality metric are kept identical to those used in generation. Notably, although multiple LLMs serve as backbones in GAG-General for generation, all generated outputs are evaluated by the same GPT-based evaluator with fixed hyperparameters, ensuring LLM-as--valuutorr
robustness and reproducibility of the assessment. The prompts used in the LLM-as- Evaluator framework are provided in Section G.3 Experiments are conducted on NVIDIA A800 with SO GB memory.
SUPPLEMENTARY EXPERIMENTAL RESULTS
F.1 SCALABILITY ANALYSIS
Following GAG (Ji et al. 2025), adopt parallel processing techniques (Gao et al. 2024 in implementing GAG-General to mitigate idle inference time LLMs during DyTAG generation. Agents are grouped into distinct clusters: each active source node agent collaborates with its corresponding destination node agent, and these groups execute in parallel across CPU cores with multiple ports, enabling an efficient DyTAG generation pipeline.
As shown in Figure D we evaluate the time costs of generating a DyTAG with 10,000 edges in TDGG and a DyTAG with 2,000 edges in IDGG, using 1,000-edge seed graph across diverse datasets with GAG-General applying GPT as the LLM backbone. Under the parallel architecture, the TDGG task achieves an average generation time of 1.61 hours, demonstrating satisfying scalability for DyTAG generation. For IDGG, the additional computational overhead of generating new nodes increases total time, which is jointly determined by both node and edge counts. Across eight datasets, the IDGG task achieves an average generation time of 1.11 hours, indicating moderate scalability with room for further optimization. Furthermore, to rigorously assess the scalability of GAG-General, we conduct experiments on generating large-scale DyTAGs in both TDGG and IDGG tasks. As illustrated in Figure for instance, generating a DyTAG with 100,000 edges in TDGG on Sephora takes 16.3 hours, while generating a DyTAG with 50,000 edges in IDGG requires 35.7 hours. Although the current time costs for large-scale DyTAG generation remain relatively high, our GDGB datasets- datasets-coompriing multiple million-edge-scae DyTAGs-provide a robust foundation for future research scalability challenges in DyTAG generation.
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edges)
edges)
Sephora
Avg: 1.61h
Sephora
1.37
Dianping
1.73
Dianping
1.49
WikiRevision
1.16
Wiki Revision
1.27
WikiLife
1.61
WikiLife
0.84
Avg: 1.11h
IMDB
IMD8
2.15
0.90
WeibeDaily
1.70
WelboDaily
0.70
Cora
1.60
Cora
1.27
1.0
1.5
2.5
0.50
0.75
1.00
1.25
1.50
1.75
(hours)
Time (hours)
Figure The time required to generate DyTAGs under TDGG (10,000 edges) and IDGG task (2,000 edges) from a 1,000-edge seed graph across GDGB datasets.
TDGG Task 1100.000 edges)
IDGG Task 150,000 edges)
Sephora
16.3
Sephora
35.7
10
12
14
16
18
20
22
10
20
25
30
35
40
Time (hours)
Time (hours)
Figure 9: The time required generate DyTAGs under TDGG (100,000 edges) and IDGG task (50,000
edges) from a 1, 1,000-ddge seed graph on Sephora and IMDB.
F.2 RESULTS OF TDGG ON GRAPH STRUCTURAL METRICS
Comprehensive results on Degree MMD. Spectra MMD, DE. and power-law validity under TDGG using three additional LLM backbones (Deepseek, Llama, and Qwen) are available in Table 19 and 20 The results demonstrate that the generated DyTAGs most datasets exhibit low Degrec/Spectra MMD values and satisfy the power-law validity criterion, confirming high-quality generation TDGG with GAG-Genene Table 18: The results on Degree MMD, Spectra MMD, DR. a, and power-law validity under TDGG with Deepseek as the LLM backbone.
Sephora
Dianping
WikiRevision
Wikilife
IMDB WeiboTTcc
WeiboDaily
Cora
Degree MMDI
0.050
0.033
0.209
0.112
0.295
0.110
0.123
0.219
Spectra MMD.
0.142
0.188
0,149
0.257
0.331
0.177
0.273
0.220
De
0.161
0.057
0.040
0.099
0.099
0.076
0.018
0.052
a Power-law Validity
2.382
2,808
2.246
2.204
2.065
2,255
2.028
2.562
Table 19: The results Degree MMD, Spectra MMD, DR. a, and power-law validity under TDGG with Llama as the LLM backbone.
Dataset
Sephora
Dianping
WikiRevision
WikiLife
IMDB
WeiboTech
WeiboDaily
Cora
Degree MMDJ
0.043
0.062
0.018
0.164
0.285
0.188
0.259
0.109
Spectra MMD.
0.010
0.399
0.048
0.217
0.316
0.257
0.421
0.126
0.135
0.044
0.108
0.125
2.976
2.331
2.328
2.204
1.750
2.145
1.979
2.328
Power-law Validity
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Table 20: The results on Degree MMD, Spectra MMD, Da. a, and power-law validity under TDGG with Qwen as the LLM backbone.
Sephora
Dianping
WikiRevision
Wikiliff
IMDB
WeiboTTcc
WeiboDaily
Cora
Degree MMDI
0.181
0.222
0.085
Spectra MMD,
0.013
0.216
0.141
0.225
0.307
0.307
0.486
0.127
D₁
0.139
0.026
0.055
0.099
0.149
0,020
0.054
0.045
a
2,978
2.177
2.065
2.204
1.723
2.020
1.844
2.367
Power-law Validity
F.3
RESULTS OF TDGG ON TEXTUAL QUALITY METRICS
Full results on average textual quality scores under TDGG are shown in Table Detailed results for each scoring crierion-CContexttie Fidelity, Personality Depth, Dynamic Adaptability, Immersive Quality, and Content Richness- rated on a 1-5 scale, are available Table 22 2 29 Full descriptions of these criteria are provided in Section [.3] The experimental results demonstrate that node memory reflection mechanisms consistently improve textual generation quality compared to their absence, highlighting the necessity of these components and the value of integrating structural and textual information in DyTAG generation.
Table 21: The full results on average textual quality scores under TDGG. M. and R. denote node memory and reflection mechanism, respectively. The best and the runner-up scores are highlighted in bold and underlined fonts.
DeepSeek w/ M.
Llama w/ M.
Qwen w/ M.
GPT
w/o M.
w/ M.R.
w/o M.
w/ M.R.
w/o M.
w/ M.R.
w/o M.
w/ M.
w/ M.R.
Sephora
4.09
4.10
4.37
4.57
4.58
4,66
4.61
4.64
4.70
4.69
4.69
4.77
Dianping
4.29
4.34
4.41
4.13
4.18
4.46
4.14
4.13
4.43
4.32
4.34
4.71
WikiRevvsion
4.36
4.31
4.66
4.73
4.93
4.79
4.96
WikiLife
4.30
4.23
4.34
4.31
4.25
4.55
4.31
4.40
4.44
4.46
4.59
IMDB
3.65
3.82
3.99
3.97
4.02
4.32
4.10
4.18
4.33
3.91
4.02
4,44
WeiboTech
3.88
3.89
3.92
4.49
4.56
4,86
4.93
4.91
4.96
4.84
4.88
4.97
WaiboDaily
4.22
4.25
Cora
4.31
4.40
4.60
4.12
4.12
4.43
4.21
4.24
4.42
3.98
4.10
4.52
Table 22: The results on each scoring criterion of generated textual quality under TDGG on Sephora.
DeepSeek w/o
Llama M.
Qwen M.
GPT
w/ M.R.
w/o M.
M.R.
w/o
w/ M.R.
M.
M.
M.R.
Contextual Fidelity
4,23
4.28
4.48
4.63
4.63
4.71
4.69
4.72
4.78
4.73
4.74
4.81
Personality Depth
3.86
3,89
4.21
4.49
4.52
4.65
4.52
4.57
4.67
4.61
4.62
4.74
Dynamic Adaptability
4.07
4.02
4.36
4.52
4.53
4.58
4.55
4.58
4.62
4.65
4.64
4.71
Immersive Quality
4.11
4.12
4.38
4.59
4.59
4.69
4.64
4.67
4.74
4.71
4.72
4.79
Content Richness
4.17
4.17
4.42
4.60
4.67
4.66
4.70
4.73
4.72
4.77
Average
4.09
4.10
4.37
4.57
4.58
4.66
4.61
4.64
4.70
4.69
4.69
4.77
36

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Published as conference paper ICLR 2026
Table 23: The results on each scoring criterion generated textual quality under TDGG on Dianping.
DeepSeek M.R.
Llama
Qwen
GPT
M.R.
M.
Contextual Fidelity
4.42
4.44
4.54
4.13
4.21
4.48
4.16
4.13
4.46
4.33
4.33
4.71
Personality Depth
4.13
4.23
4.28
4.02
4.06
4.47
4,03
3.99
4.36
4.19
4.21
4.66
Dynamic Adaptability
4.24
4.28
4.34
4.09
4.16
4.38
4.06
4,07
4.33
4.25
4.30
4.68
Immersive Quality
4.28
4.35
4.42
4.15
4.19
4.47
4.16
4.15
4,45
4.34
4.34
4.72
Richness
Average
4.29
4.34
4.41
4.13
4.18
4.46
4.14
4.13
4.43
4.32
4.34
4.71
Table 24: The results on each scoring criterion of generated textual quality under TDGG on WikiRevision.
DeepSeck w/
Llama w/ M.
Qwen w/ M.
GPT
wofo M.
w/ M.R.
w/o M.
M.R.
w/o
w/ M.R.
w/o M.
M.
M.R.
Fidelity
Personality Depth
4,16
4.16
4.32
4.12
4.21
4.33
4.59
4.62
4.92
4.67
4.72
4.95
Dynamic Adaptability
4.12
4.11
4.32
4.02
4.10
4.55
4.60
4.66
4.72
4,94
Inmerssie Quality
4.21
4.21
4.38
3.98
4.06
4.21
4.69
4.73
4.94
4.77
4.82
4.97
Content Richness
4.21
4.20
4.38
4.30
4.72
4.73
4.94
4.79
4.81
4.%6
Average
4.19
4.36
4.74
4.79
Table 25: The results on each scoring criterion of generated textual quality under TDGG WikiLife.
DeepSeek w/ M.
Llama w/ M.
Qwen w/ M.
GPT
w/o M.
w/ M.R.
w/o M.
w/ M.R.
w/o M.
w/ M.R.
w/o M.
w/ M.
w/ M.R.
Contextual Fidelity
4.43
4.34
4.43
4.47
4.44
4.65
4.40
4.44
4.48
4.54
4.58
4.66
Personality Depth
4.29
4.19
4.31
4.31
4.27
4.59
4.29
4.39
4.41
4,58
Dynamic Adaptability
4.08
4.04
4.19
4.10
4.10
4.35
4.14
4.13
4.24
4.25
4.28
4.45
Immersive Quality
4.39
4.42
4.54
4.54
Content Richness
4.33
4,30
4.38
4.26
4.25
4.54
4.31
4.34
4.42
4,46
4.46
4.61
Average
4.30
4.23
4.34
4.31
4.25
4.55
4.31
4.33
4.40
4.44
4.46
4.59
Table 26: The results on each scoring criterion of generated textual quality under TDGG on IMDB.
DeepSeek
Llama M.
Qwen M.
GPT
w/o M.
w/ M.R.
w/o M.
M.R.
w/o
w/ M.R.
M.
M.
M.R.
Contextual Fidelity
3.93
4.15
4.30
4.22
4.31
4.50
4.42
4.48
4.55
4.14
4.28
4.61
Personality Depth
3.49
3,62
3.80
3.83
3.86
4.38
3.95
4.03
3.78
3.88
4.48
Dynamic Adaptability
3.45
3.63
3.78
3.76
3.80
4.04
3.88
3.95
4.06
3,69
3,82
4.12
Immersive Quality
3.72
3.88
4.04
4.07
4.13
4.47
4.22
4.29
4.46
4.01
4.15
4.58
Content Richness
3.64
3.83
4.03
3.97
4.00
4.24
4.06
4.14
4.27
3.92
3.97
4.38
Average
3.65
3.82
3,99
3.97
4.02
4.32
4.10
4.18
4.33
3.91
4.02
4.44
37

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Published as conference paper ICLR 2026
Table 27: The results on each scoring criterion of generated textual quality under TDGG on WeiboTeech
DeepSeek M.R.
Llama
Qwen
GPT
M.R.
M.
Contextual Fidelity
4.09
4,08
4.12
4.59
4.66
4.88
4.94
4.92
4.97
4.86
4.90
4.99
Personality Depth
3.96
3.95
3.97
4.50
4.54
4.90
4.91
4.89
4.95
4.83
4.86
4,97
Dynamic Adaptability
3.62
3.63
3,70
4.40
4.50
4.82
4.93
4.91
4.95
4.83
4.88
4,97
Immersive Quality
3.95
3.95
4.00
4.55
4.62
4.88
4.93
4.92
4.96
4,85
4.88
4,98
Richness
Average
3.88
3.89
3.92
4.49
4.56
4.86
4.93
4.91
4.96
4.84
4.88
4.97
Table 28:
The results on each scoring criterion of generated textual quality under TDGG on WeiboDaily.
DeepSeek w/ M.
Qwen w/ M.
GPT w/ M.
wlo M.
w/ M.R.
w/o M.
w/ M.
w/ M.R.
w/o M.
w/ M.R.
w/o M.
w/ M.R.
Contextual Fidelity
4.16
4.38
4.40
4,61
4.66
4,92
4.90
4.89
4.98
4.83
4.93
5.00
Personality Depth
4.05
4.27
4.33
4.87
4.98
4.79
4,90
4,99
Dynamic Adaptability
3.65
3.98
3.97
4.44
4.51
4.87
4.88
4.87
4.97
4.78
4.91
4.97
Immersive Quality
4.37
4.83
4.92
Content Richness
3.90
4.18
4.18
4.41
4.50
4.86
4.88
4.87
4.97
4.77
4.91
4.97
Average
3.95
4.22
4.25
4.51
4.58
4.90
4.88
4.88
4.98
4,80
4.92
4.99
Table 29: The results on each scoring criterion of generated textual quality under TDGG on Cora.
DeepSeek w/ w/ M.R.
Llama w/ M.
Qwen w/ M.
GPT
wrla M.
w/o M.
M.R.
w/o M.
w/ M.R.
w/n M.
w/ M.
w/ M.R.
Contextual Fidelity
4.40
4.49
4.21
4.19
4.28
4.32
4.49
4.08
4.18
Personality Depth
4.25
4.32
4.53
4.15
4.13
4.45
4.21
4.25
4.43
3,99
4.11
4.52
Dynamic Adaptability
4.23
4.28
4.47
3.88
3.93
4.23
4.04
4,01
4.25
3.73
3.89
4.33
Immersive Quality
4.33
4.42
4.64
4.20
4.18
4.49
4.26
4.32
4.48
4.06
4.17
4,58
Content Richness
4.35
4.49
4.69
4.18
4.17
4.49
4.25
4.30
4.46
4.05
4.16
4.59
Average
4.31
4.40
4.60
4.12
4.12
4.43
4.21
4.24
4.42
3.98
4.10
4.52
F.4 RESULTS OF TDGG ON GRAPH EMBEDDING METRICS
Full results on the graph embedding metric under TDGG are shown in Table 30 Similar to the findings on textual quality, the of node memory and reflection mechanisms significantly enhances generation quality, underscoring the critical role integrating structural and textual information in DyTAG generation.
Table 30: The results on the graph embedding metric under TDGG.
DeepSeek w/ M.
Llama w/ M.
Qwen w/ M.
GPT
w/o M.
w/ M.R.
w/o M.
w/ M.R.
w/o M.
w/ M.R.
w/o M.
w/ M.
w/ M.R.
Sephora
0.715
0.758
0.679
(.675
0.681
0.648
0.579
0.630
0.740
0.588
0.671
0.654
Dianping
0.666
0.722
0.368
0.386
0.408
0.408
0.390
0.413
0.351
0.368
0.369
WikiRevision
0.641
0.624
0.567
0.582
0.636
0.458
0.429
0.464
0.422
0.454
WikiLife
0,599
0.554
0.588
0.429
0.462
0.477
0.443
0.478
0.490
0.459
0.453
0.463
IMDB
0.534
0.586
0,532
0.396
0.482
0.533
0.487
0.493
0.519
0.432
0.420
0.435
WeiboTech
0.415
0.577
0,501
0.335
0.417
0.420
0.296
0.353
0.364
0.488
0.698
WaiboDaily
0.471
0.424
0.494
01.403
0.524
0.494
0.437
0.377
0.389
0.681
0.689
0.694
Corn
0.649
0,556
0,581
0.689
0.499
0.483
0.485
0.450
0,536
0.511
0.445
0.465
38

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Published as conference paper ICLR 2026
F.5 RESULTS OF TDGG ON DIRECT USABILITY IN DOWNSTREAM TASKS
To evaluate the direct usability of DyTAGs generated under TDGG task, we assess their effectiveness
substitutes for original graphs in standard transductive downstream tasks (edge classification and link prediction), following the conventional experimental settings in dynamic graph learning (Yu et al. 2023
Zhang al. 2 Peng et al. 2025). We employ four established dynamic graph neural network (DGNN)
models TGN (Rossi et al. 2020). CAWN (Wang et al. 2021a). GraphMixer (Cong et al.) 2023), ) and
DyGFormer (Yu et al. 2023) to perform edge classification and link prediction both the generated (Gen.)
and original (Ori.) graphs across four datasets: Sephora, WikiLife, Cora, and WeiboTech.
For edge classification, the results in Table 31 show consistently small performance gaps between the generated and original graphs. The absolute differences Precision (lAP), Recall ( Rl). and Fl-score (IAF)) are uniformly below 0.04 across all models and datasets, with most values under 0.02. This indicates that the generated graphs preserve sufficient structural and textual fidelity to support accurate classification. Similarly, in link prediction (transductive setting dynamic graph learning). the Average Precision (AP) differences (PA) between using generated and original graphs are minimal, as summarized in Table 32 The maximum observed AAP 0.025, further confirming that the generated Dy DyTAGs accurately capture the temporal evolution patterns of the original graphs. graphs.These These results demonstrate that DyTAGs generated by our framework in the TDGG task are of high quality and can be directly deployed in downstream applications with negligible performance degradation.
F.6
RESULTS OF NODE RETRIEVAL
Comprehensive results on the node retrieval task, including Hit@ and Hit@ 10 metrics, are presented in Table 33 Detailed results for all LLM backbones and DGNN baselines provided in Table 34 36 The experimental findings reveal that DGNNS maintain a significant advantage in node retrieval pertormance. However, with reduced training data availability, the GAG-General model achieves superior results than DGNNs on the Sephora and IMDB datasets under a generative paradigm. This highlights the strong dependency of DGNNs large-scale training data and their limited generalization capability in low-data scenarios.
Table 33: The results on Hit@ and Hit @ 10 under the node retrieval task. Ours correspond to the best results of our proposed GAG-General among four LLM backbones.
1
Hit@10
Ours
TGN
CAWN
GraphMixer
DyGFormer
Ours
TGN
CAWN
GraphMixer
DyGFormer
Sephora
0.285
0.045
0,023
0.065
0.028
0.386
0.259
0.206
0.309
0.184
Dianping
0.176
0.040
0.033
0.036
0,534
0.178
0.188
0.193
0.207
0.585
WikiRevision
0.105
0.031
0.067
0.439
0.230
0.164
0.287
0.272
0.507
Wikil life
0.139
0.119
0.116
0.175
0.160
0.315
0.388
0.379
0.511
0.264
IMDB
0.142
0.015
0.009
0.005
0.008
0.159
0.123
0.110
0.105
0.111
Weiho Tech
0.051
0.183
0.128
0.169
0.672
0.056
0.371
0.448
0.378
0.678
Waibo
0.091
0.138
0.267
0.445
0.154
0.100
0.295
0.615
0.663
0.438
Cora
0.122
0.025
0.016
0.064
0.465
0.237
0.193
0.175
0.299
0.321
39

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Published as conference paper ICLR 2026
Table 31: The result of edge classification performance on generated graphs from TDGG and original graphs.
Model
Dataset
Type
Precision
Recall
F1
[AP] 4
[AR]
AF
Gen.
0,556 i 0.009
0.490 0.012
0.523 = 0.004
Sephora
0.022
0.004
0.011
Oni.
0.534 + 0.009
0.494 0.036
0,512 = 0.026
Gen.
0.176 + 0.024
0.299
0.017
0.191 = 0.033
WikiLife
0.033
0.017
0.030
TGN
0.209 = 0.016
0.035 0.282
= 0.010
Gen.
0.219 = 0.016
0,241
0.004
0.206 = 0.027
Cora
0.014
0.009
0.016
0.233 + 0.021
0.232 0.017
0.190 0.033
Gen.
0.781 + 0.008
0.790 0.019
0.782 0.012
WeiboTecch
0.004
0.019
0.026
Ori.
0.777 + 0.009
0.771 0.024
0.756 = 0.012
Gen.
0.526 +: 0.017
0.690
0.016
0.563 + 0.014
Sephora
Ori.
0.531 + 0.029
0.696
0.003
0.536 = 0.023
0.005
0.006
0.007
Gen.
0.190 + 0.015
0.292 + 0.016
0.206 + 0.023
WikiLife
0.009
0.008
0.022
CAWN
Ori.
0.199 +: 0.003
0.284
0.033
0.228 + 0.026
Gen.
0.212 + 0.014
0.038 0.269
0.278
0.188 + 0.032
Cora
0.010
0.009
0.016
0.222 +:
0.031
+
WeiboTeeh
Gen.
0.783 + 0.018
0.787
0.017
0.784 = 0.019 0.767 0.031
0.013
0.002
0.017
Oni.
0.770 + 0.020
0.789 0.020
Sephora
Gen.
0.576 + 0.024
0.690
0.003
0.563 0.018
0.023
0.005
0.012
Ori.
0.553 = 0.020
0.017 0.685
0.575 = 0.033
WikiLife
Gen.
0.227 = 0.015
0.315
0.005
0.218 = 0.021
0.014
0.009
GraphMixer
Ori.
0.213 = 0.015
0,301
0.013
0.209 = 0.008
Gen.
0.222 + 0.014
0.268
0.015
0.222 + 0.021
Cora
Ori.
0.235 = 0.009
0.260 0.033
0.224 = 0.012
0.013
0.008
0.002
Gen.
0.776 + 0.018
0.787
0.036
0.775 + 0.026
WeiboTech
0.005
0.007
0.002
0.780
0.007
Sephora
Gen.
0.476 + 0.011
0.690
0.004
+ 0.029
0.007
0.008
0.003
Oni.
0.469 + 0.025
0.007 0.698
0.560 0.017
Wikil
Gen.
0.242 i 0.024
0.311
0.014
0.194 + 0.029
0.003
0.000
0.002
DyGFormer
Oni.
0.239 +:t 0.029
0.020 0.311
0.196 = 0.017
Gen.
0.209 +:t 0.018
0.274
0.039
0.206 + 0.023
Cora
0.010
0.024
0.007
Ori.
0,219 + 0.021
0,250 : 0.024
0.213 = 0.030
Gen.
0.806 1 0.010
0.803
0.024
0.780 + 0.014
WeiboTech
0.020
0.013
0.013
0.786 + 0.007
0.790 0.006
0.767 = 0.006
Table 34: The results on Hit@l under the node retrieval task with our framework.
DeepSeek w/ M.
I Llama w/ M.
Qwen w/ M.
GPT
w/o M.
w/ M.R.
w/o M.
w/ M.R.
w/o M.
w/ M.R.
w/o M.
w/ M.
w/ M.R.
Sephora
0.002
0.285
0.048
0.000
0.039
0.073
0.019
0.046
0.064
0.016
0.066
0.077
Dianping
0.000
0.176
0.094
0.001
0.002
0.003
0.001
0.008
0.041
0.001
0.002
0.013
WikiRevision
0.003
0.013
0.005
0.084
0.105
0.013
0.009
0.013
0.015
WikiLife
0.029
0.135
0.034
0,086
0.094
0.097
0.067
0.073
0,075
0.107
0.126
0.139
IMDB
0.000
0.142
0.020
0.001
0.022
0.005
0.043
0.043
0.053
0.001
0.017
0.019
WeiboTech
0.002
0.051
0.021
0.001
0.002
0.001
0.004
0.004
0,005
0.007
0.008
WaiboDaily
0.009
0.091
0.040
0.003
0.006
0.003
0.009
0.012
0,013
0.002
0.014
0.004
Cora
0.002
0.069
0.082
0.000
0.024
0.033
0.110
0.108
0.122
0.000
0.045
0.115
40

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Published as conference paper ICLR 2026
Table 32: The results of link prediction performance (transductive) on generated graphs from TDGG and original graphs.
Model
Dataset
Type
Average Precision
AAAP
Gen.
0.663 + 0.025
Sephora
0.010
Ori.
0.673 0.016
WikiLife
0.017
TGN
Ori.
0.790 + 0.023
Gen.
0.621 + 0.022
Cura
0.025
Ori.
0.596 + 0.012
Gen.
0.859 + 0.009
WeiboTech
0.021
Ori.
0.838 1: 0.027
Gen.
0.731 = 0.017
Sephora
0.001
Ori.
0.730 + 0.015
Gen.
0.831 + 0.003
WikiLife
0.019
CAWN
Ori.
0.850 + 0.023
Gen.
0.616 + 0.011
Cora
0.024
Ori.
0.592 = 0.017
Gen.
0.862 + 0.020
WeiboTesch
0.013
Ori.
0.030
Gen.
Sephora
0.018
Ori.
0.711 + 0.010
Gen. Ori.
0.813 0.002
WikiLife
0.018
GraphMixer
0.795 + 0.030
Gen.
0.626 1: 0.030
Cora
0.002
Ori.
0.624 + 0.021
Gen.
0.868 = 0.028
WeiboTech
0.010
Ori.
0.858 + 0.011
Gen.
0.631 + 0.007
Sephora
0.013
Ori.
0.644 1: 0.027
WikiLife
Gen.
0,800 + 0.022
0.004
DyGFormer
Ori.
0.796 = 0.012
Gen.
0.824 + 0.020
Cora
0.004
Ori.
0.027
WeiboTech
Gen.
0,800
0.001
Ori.
0.799 + 0.009
Table 35: The results on Hit 10 under the node retrieval task with our framework.
DeepSeek w/ M.
Llama w/ M.
Qwen w/ M.
GPT
w/o M.
w/ M.R.
w/o M.
w/ M.R.
w/o M.
w/ M.R.
w/o M.
w/ M.
w/ M.R.
Sephora
0.064
0.386
0.169
0.090
0.164
0.314
0.085
0.191
0.278
0.071
0.291
0.369
Dianping
0.004
0.178
0.108
0.003
0.004
0.014
0.002
0.015
0.061
0.004
0.009
0.024
WikiRevision
0.041
0.031
0.143
0.067
0.065
0.077
WikiLife
0.096
0.205
0.093
0.216
0.255
0.260
0.145
0.156
0.178
0.238
0.275
0.315
IMDB
0.035
0.159
0.063
0.015
0.033
0.021
0.055
0.054
0.064
0.012
0.025
0.029
WeiboTech
0.006
0.056
0.028
0.007
0.011
0.026
WaiboDaily
0.021
0.100
0.049
0.015
0.024
0.012
0.022
0.021
0.019
0.036
0.022
0.016
Corn
0.064
0.128
0.160
0.111
0.135
0.174
0.205
0.209
0.226
0.128
0.172
0.237
41

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Published as a conference paper ICLR 2026
Table 36: The results on @it@ I and @it@ 10 under the node retrieval task with DGNN baselines.
Hit@1 1
Hit@ 10
JODIE
TGN
CAWN
GraphMixer
DyGFormer
JODIE
TGN
CAWN
GraphMixer
DyGFommer
Sephora
0.018
0.045
0.023
0.065
0.028
0.120
0.259
0.206
0.309
0.184
Dianping
0.419
0.040
0.033
0.036
0.534
0.427
0.188
0.193
0.207
0.585
WikiRevision
0.284
0.031
0.067
0.099
0.164
0.287
0.507
WikiLife
0.049
0.119
0.116
0.175
0.160
0.410
0.388
0.379
0.511
0.264
IMDB
0.013
0.015
0.009
0.005
0.008
0.111
0.123
0.110
0.105
0,111
Weibo
0.632
0.183
0.128
0.169
0.672
0.650
0.371
0.448
0.378
0.678
WailyDaily
0.027
0,138
0.267
0.445
0.154
0.440
0.295
0.615
0.663
0.438
Cora
0.017
0.025
0.016
0.064
0.129
0.175
0.299
0.321
F.7 RESULTS OF EDGE CLASSIFICATION
Full results on Precision, Recall, and F1 metric under the edge classification task are available in Table 37 Detailed results on all LLM backbones and DGNN baselines are available in Table and 39 The experimen- tal findings demonstrate that our GAG-General model achieves excellent performance on this discriminative task under the generative paradigm. This success stems from the strong correlation between edge labels and the semantic information encoded in both node texts and generated edge texts. For instance, e-commerce recommendation scenarios, if negative review is generated the edge text, the corresponding edge label (e.g., low rating) aligns closely with this sentiment. In contrast, existing DGNNs struggle to effectively capture and utilize the semantic richness of node/edge texts in DyTAG datasets, resulting in significant performance gaps on edge classification tasks.
Table 37: The results on Precision, Recall, and F1 metric under the edge classification task. Ours correspond the best results of our proposed GAG-General among four LLM backbones.
Datasets
Models
Ours
JODIE
TGN
CAWN
GraphMixer
DyGFormer
Precision
0.798
0.463
0.452
0.442
0.443
0.471
Sephora
Recall
0.790
0.661
0.672
0.661
0.684
0.687
F1
0.790
0.555
0.529
0,532
0.546
0.559
Precision
0.572
0.359
0.345
0.409
0.325
0.217
Dianping
0.437
0.466
F1 0.522
0.341
0.279
0.330
0.298
0.296
Precision
0.804
0.291
0.474
0.739
0.756
0)4011
WikiRevision
Recall
0.829
0.378
0.387
0.444
0.382
0.431
0.782
0.209
0.232
0.341
0.217
0.303
Precision
0.562
0.151
0.171
0.199
0.194
0.093
WikiLife
Recall
0.451
0.255
0.288
0.290
0.291
0,284
0.484
0.163
0.188
0.209
0.172
0.128
Precision
0.698
0.423
0.442
0.470
0.473
0.463
IMDB
Recall
0.666
0.680
0.672
0.677
0.650
0.682
0.672
0.531
0.542
0.551
0.546
0,533
Precision
0.726
0.615
0.479
0.479
0.503
0,533
WeiboTechh
Recall
0.779
0.687
0.692
0.690
0.692
0.692
F]
0.740
0.587
0.566
0.566
0.566
0,568
Precision
0.980
0.853
0.853
0.867
0.853
0.898
WeiboDaily
Recall
0.990
0.924
0.924
0.922
0.924
0.925
F1
0.985
0.887
0.887
0.891
0.887
0.890
Precision
0.566
0.186
0.149
0.149
0.153
0.099
0.273
0.280
0.279
0,280
F1
0,539
0.152
0.148
0.134
0.131
0.126
42

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Published as conference paper ICLR 2026
Table 38:
The results on Precision, Recall, and Fl metric under the edge classification task on all LLM backbones.
DeepSeek w/ M.
Ilama w/ M.
Qwen
GPT
w/o M.
w/ M.R.
w/o M.
W/ M.R.
w/o M.
w/ M.
w/ M.R.
wlo M.
w/ M.
w/ M.R.
Procision
0.714
0.744
0.798
Sephora
Recall
0.682
0.742
0.790
0.628
0.628
0.637
0.386
0.397
0.400
0.593
0,586
0.591
0.686
0.734
0.790
0.620
0.620
0.625
0.564
Precision
0.541
0.572
0.531
0.427
0.462
0.455
0.410
0.422
0.425
0.409
0.396
0.393
Dianping
Recall
0.529
0.330
0.533
0.346
0.352
0.380
0.408
0.432
0.422
0.374
0,343
0.354
0.521
0.411
0.522
0.260
0.271
0.291
0.384
0.411
0. 0.397
0.311
0,286
0.288
Precision
0.750
0.715
0.736
0.712
0.717
0.804
0.587
0.605
0.625
0.681
0.678
0.689
Wikiieevisio
0.741
F1
0.727
0.681
0.712
0.695
0.686
0.782
0.429
0.437
0.444
0.410
0.404
0.420
Procision
0.411
0.414
0.438
0.537
0.537
0.393
0.416
0.414
0.544
0.549
WikiLife
Recall
0.387
0.386
0.415
0.240
0.254
0.243
0.246
0.249
0.253
0.442
0.441
0.451
0,381
0.380
0.409
0.292
0.306
0.297
0.210
0.219
0.215
0.472
0.475
0.484
Precision
0.640
0.666
0.698
0.634
0.686
0.647
0.683
0.681
0.647
0,550
0.617
0.606
IMDB
Recall
0.573
0.615
0.657
0.431
0.529
0.566
0.666
0.664
0.665
0.427
0.526
0.576
0,598
0.635
0.672
0.497
0.572
0.595
0.626
0.620
0.621
0.466
0.547
0.577
Precision
0.676
0.675
0,688
0.629
0.701
0.631
0.644
0.693
0.642
0,626
0.636
0.726
WeihoTech
0.697
FI
0.685
0.679
0.693
0.589
0.646
0.574
0,583
0.583
0.595
0,590
0,584
0.740
Precision
0.918
0.913
0.923
0.902
0.925
0.927
0.888
0.935
0.906
0.980
0.902
0,980
WeihoDuily
Recall
0.918
0.916
0.931
0.924
0.948
0.921
0.930
0.931
0,931
0,990
0,931
0,990
0.918
0.914
0.927
0.891
0.923
0.883
0.896
0.897
0.898
0.985
0.899
0.985
Precision
0.566
0,542
0.528
0.460
0,456
0.377
Cura
Recall
0,550
0.474
0.520
0.473
0.431
0.414
0.414
0.427
0.436
0.421
0.377
0.386
43

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Published as conference paper ICLR 2026
Table 39: baselines.
The results on Precision, Recall, and F1 metric under the edge classification task on all DGNN
Models
JODIE
TGN
CAWN
GraphMixer
DyGFormer
Precision
0.463
0.452
0.442
0.443
0.471
Sephora
Recall
0,661
0.672
0.661
0,684
0.687
F1
0.555
0.529
0,532
0,546
0.559
Precision
0.359
0.345
0.409
0.325
0.217
Dianping
Recall
0.437
0.436
0.466
0.466
Fl
0.341
0.279
0.330
0.298
0.296
Precision
0.291
0,474
0,739
0.756
0.401
WikiRevision
Recall
0.378
0.387
0.444
0.382
0.431
F1
0.209
0.232
0.341
0,217
0.303
Precision
0.151
0.171
0.199
0.093
WikiLiife
Recall
0,255
0.288
0.290
0.291
0.284
0.163
0.188
0.209
0.172
0.128
Precision
0.423
0.442
0.470
0.473
0.463
IMDB
Recall
0.680
0.672
0.677
0.650
0.682
F1
0,531
0.542
0.551
0,546
0,533
Precision
0.615
0.479
0.4799
0.503
0.533
WeiboTech
Recall
0.687
0.692
0.690
0.692
0.692
F1
0.587
0.566
0.566
0.566
0.568
Precision
0.853
0.853
0.866
0.853
0.898
WaiboDaily
Recall
0.924
0.924
0,922
0.924
0.925
0.887
0.887
0.891
0.887
0.890
Precision
0.186
0.149
0.149
0.153
0.099
Cora
Recall
0,273
0.280
01,274
0.279
0,280
F1
0.152
0.148
0.134
0,131
0.126
F.8 RESULTS OF IDGG ON GRAPH STRUCTURAL METRICS
Full results on Degree MMD, Spectra MMD, DR. a, power-law validity under IDGG using other three LLM backbones are available in Table 40 41 and 42 Compared to the TDGG task, DyTAGs generated under IDGG exhibit slightly higher Degree/Spectra MMD values, primarily due to the introduction of new node generation, which increases the complexity of the modeling process. Nevertheless, over half of the generated DyTAG graphs still satisfy the power-law distribution criterion, demonstrating the robustness framework in maintaining structural quality even under more challenging generation
of the conditions.
Table 40: The results on Degree MMD, Spectra MMD, Dr. a, and power-law validity under IDGG with Deepseek as the LLM backbone.
Dataset
Sephora
Dianping
WikiRevision
WikiLife
IMDB
WeiboTech
WeiboDaily
Cora
Degree MMD
0.370
0.150
0.191
0.087
0.329
0.212
0.219
0.073
Spectra MMD
0.250
0.351
0.158
0.206
0.440
0.172
0.397
0.181
0.056
0.029
0.131
0.084
2.135
3,010
2.185
2.204
1.805
1.953
1.839
2.337
Power-law Validity
X
X
44

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Published as conference paper ICLR 2026
Table 41: The results on Degree MMD, Spectra MMD, Dj. a, and power-law validity under IDGG with Llama the LLM backbone.
Dataset
Sephora
Dianping
WikiRevision
Wikiliif
IMDB
WeiboTTec
WeiboDaily
Cora
Degree MMD
0.236
0.246
Spectra MMD
0.243
0.413
0.105
0.223
0.379
0.215
0.385
0.232
D2
0.098
0.067
0.103
0.099
0.226
0.029
0.088
0.130
a
2.077
2,480
2,300
2.204
1.972
2.099
1.968
2.174
Power-law Validity
Table 42: The results Degree MMD, Spectra MMD, DR, a, and power-law validity under IDGG with Qwen as the LLM backbone.
Dataset
Sophora
Dianping
WikiRevision
WikiLiff
IMDB
WeiboTech
WeiboDaily
Cora
Degree MMD
0.512
0.321
0.286
0.083
0.303
0.237
0.295
0.095
Spectra MMD
0.234
0.436
0.193
0.218
0.415
0,189
0.461
0.192
D&
0.076
2.015
2,497
2.114
2.204
1.783
1,900
1.757
2.247
Power-law Validity
X
X
F,9 RESULTS OF IDGG ON TEXTUAL QUALITY METRICS
Full results on average textual quality scores under IDGG are shown in Table Detailed results on each scoring criterion are available in Table [45 Similar the findings under the TDGG task, the experimental results demonstrate that incorporating node memory or reflection mechanisms significantly enhances the textual quality of generated DyTAGs, highlighting the importance of integrating structural and textual information in Dy TAG generation.
The results on average textual quality scores under IDGG. M. and R. denote node memory and memory reflection mechanism, respectively.
Table 43:
DeepSeek
Llama
Qwen M.
GPT
M.
Sephora
4.73
4.77
4.58
4.65
4.74
4.58
4.78
4.58
4.77
4.87
Dianping
4,29
4.44
4.65
4.29
4.51
4.87
4,04
4.50
4.68
4.56
4.71
4.86
WikiRevision
4.21
4.46
4.63
3.98
4.21
4.30
4.37
4.53
4.71
4.39
4.54
4.71
Wikil afe
4.15
4.30
4.44
4.12
4.26
4.38
4.11
4.26
4.29
4.28
4.39
4.47
IMDB
4.13
4.28
4.39
4.22
4.23
4.51
4.29
4.49
WeiboTech
4.60
4.75
4.85
3.94
4.00
4.75
4.56
4.74
4.93
4.60
4.71
4.93
WeiboDaily
4.68
4.81
4.92
4.32
4.43
4.82
4.75
4.87
4.98
4.74
4.83
4.99
Cora
4.56
4.26
4.29
4.27
4.36
4.57
Table 44: The results on each scoring criterion of generated textual quality under IDGG on Sephora.
DeepSeek w/o M.
Llama w/ M.
Qwen M.
GPT
w/ M.R.
w/o M.
M.R.
w/o
w/ M.R.
w/o M.
M.
M.R.
Contextual Fidelity
4.63
4.80
4.87
4.64
4.73
4.83
4.51
4.78
4.92
4.77
4.84
4.93
Personality Depth
4.53
4.56
4.61
4.55
4.52
4.63
4.42
4.46
4.60
4.20
4.61
4.78
Dynamic Adaptability
4.60
4.67
4.70
4.41
4.59
4,66
4.55
4.61
4.69
4.54
4.71
4,82
Immersive Quality
4.71
4.83
4.87
4.71
4.73
4.84
4.77
4.81
4.91
4.69
4.86
4.95
Content Richness
4.66
4.79
4.81
4.59
4.67
4.74
4.66
4.74
4.79
4.72
4.80
4.86
Average
4.63
4.73
4.77
4.58
4.65
4.74
4.58
4.68
4.78
4.58
4.72
4.87
45

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Published as conference paper ICLR 2026
Table 45: The results on each scoring criterion of generated textual quality under IDGG on Dianping.
DeepSeek M.R.
Llama
Qwen
GPT
M.R.
M.
Contextual Fidelity
4.40
4.50
4.85
4.52
4.64
4.95
3.89
4.57
4.77
4.56
4.72
4.93
Personality Depth
4.03
4.21
4.32
4.33
4.21
4.72
4.01
4,21
4.45
4.33
4.42
4.69
Dynamic Adaptability
4.21
4.37
4.50
4.28
4.39
4.82
3.98
4.43
4.61
4.50
4.69
4.80
Immersive Quality
4.47
4.37
4,81
4.11
4.67
4,95
4.22
4.60
4.79
4.70
4.82
4,94
Richness
Average
4.29
4.44
4.65
4.29
4.51
4.87
4.04
4.50
4.68
4.56
4.71
4.86
Table 46: The results each scoring criterion of generated textual quality under IDGG on WikiRevision.
DeepSeck
Llama w/ M.
Qwen w/ M.
GPT
w/o M.
w/ M.R.
w/o M.
M.R.
w/o
w/ M.R.
w/o M.
M.
M.R.
Fidelity
4.88
Personality Depth
4,01
3.81
4.09
3.68
3.65
3.75
3.81
3.90
4.17
3.89
3.91
4.20
Dynamic Adaptability
3.92
4.28
4.44
3.76
3.91
4.01
4.21
4.32
4.10
4.33
Inmerssie Quality
4.23
4.81
4.91
4.20
4.60
4.69
4.62
4.86
4.96
4.83
4.89
4.96
Content Richness
4.01
4.53
4.76
4.05
4.14
4.21
4,40
4.66
4.86
4.29
4.64
4.86
Average
4.21
3.98
4.30
4.37
4.39
4.54
Table 47: The results each scoring criterion of generated textual quality under IDGG WikiLife.
DeepSeek w/ M.
Llama w/ M.
Qwen w/ M.
GPT
w/o M.
w/ M.R.
w/o M.
w/ M.R.
w/o M.
w/ M.R.
w/o M.
w/ M.
w/ M.R.
Contextual Fidelity
4.52
4.67
4.83
4.53
4.63
4.63
4.30
4.58
4.57
4.62
4.74
4.79
Personality Depth
3.63
3.77
3.90
3.62
3.75
4.07
3.81
3.73
3.85
4.00
Dynamic Adaptability
4.04
4.03
4.11
3.92
4.02
4.11
3.92
4.02
4.06
4.02
4.10
4.17
Immersive Quality
4.34
Content Richness
4.24
4.38
4.57
4.13
4.28
4.46
4.33
4.39
4.43
4.42
4.55
4.66
Average
4.15
4.30
4.44
4.12
4.26
4.38
4.11
4.26
4.29
4.28
4.39
4.47
Table 48: The results on each scoring criterion of generated textual quality under IDGG on IMDB.
DeepSeek w/o M.
Llama M.
Qwen M.
GPT
w/ M.R.
w/o M.
M.R.
w/o
w/ M.R.
w/o M.
M.
M.R.
Contextual Fidelity
4.53
4.71
4.88
4.76
4.83
4.84
4.82
4,80
4.88
4.65
4.70
4.78
Personality Depth
3.72
3,81
3.84
3.67
3.76
4.03
3.78
3.87
4.17
3.72
3.82
4.19
Dynamic Adaptability
3.89
4,06
4.10
4.01
4.04
4.16
4.02
4.17
4.25
3,90
4.08
4.23
Immersive Quality
4.30
4.65
4.82
4.58
4.72
4.80
4.33
4.71
4.85
4.56
4.65
4.77
Content Richness
4.20
4.18
4.29
4.10
4.20
4.30
4.19
4.26
4.40
4.11
4.18
4.48
Average
4.13
4.28
4.39
4.22
4.31
4.43
4.23
4.36
4.51
4.19
4.29
4.49
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Table 49: The results on each scoring criterion generated textual quality under IDGG on WeiboTech.
DeepSeek M.R.
Llama
Qwen
GPT
M.R.
M.
Contextual Fidelity
4.77
4.89
4.94
4.22
4.23
4.83
4.89
4.90
4.99
4.82
4.91
4.99
Personality Depth
4.30
4.50
4.72
3.65
3.76
4.68
4.22
4.49
4.83
4.33
4.43
4,84
Dynamic Adaptability
4.42
4.75
4.84
3.95
3,96
4.70
4.35
4.77
4.94
4.65
4.73
4,92
Immersive Quality
4.83
4.87
4,93
4.02
4.16
4.82
4.76
4.89
4,98
4.67
4.88
4,99
Richness
Average
4,60
4.75
4.85
3.94
4.00
4.75
4.56
4.74
4.93
4.60
4.71
4.93
Table 50: The results on each scoring criterion of generated textual quality under IDGG on WeiboDaily.
DeepSeek w/ M.
Llama w/ M.
Qwen w/ M.
GPT
w/o M.
w/ M.R.
w/o M.
M.R.
w/o M.
w/ M.R.
w/o M.
w/ M.
w/ M.R.
Contextual Fidelity
4.82
4.90
4.96
4.34
4.57
4.86
4.82
4.93
4.99
4,82
4.90
4,99
Personality Depth
4.97
Dynamic Adaptability
4,64
4.80
4.91
4.42
4.38
4.79
4.82
4.86
4.97
4.71
4.85
4,99
Immersive Quality
4.82
4.90
4.96
4.32
4.56
4.86
4.72
4.94
4.99
4.82
4.50
4,99
Content Richness
4.62
4.77
4.91
4.32
4.34
4.79
4.68
4.85
4.97
4.67
4.80
4,98
Average
4.68
4.81
4.92
4.32
4.43
4.82
4.75
4.87
4.98
4.74
4.83
4.99
Table 51: The results each scoring criterion of generated textual quality under IDGG on Cora.
DeepSeek w/ M.
Llama w/ M.
Qwen w/ M.
GPT w/ M.
w/o M.
w/ M.R.
w/o M.
w/ M.R.
w/o M.
w/ M.R.
w/o M.
w/ M.R.
Contextual Fidelity
4.64
4.84
4.94
4.61
4.77
4.92
4.63
4,82
4.91
4.72
4.78
4,93
Personality Depth
3.69
3.71
3,80
3.57
3.60
3.74
3.51
3.76
3.68
3.58
3.80
Dynamic Adaptability
4.25
4.26
4.36
4.20
4.21
4.44
4.24
4.28
4.40
4.11
4.19
4.43
Immersivv Quality
4.81
4.65
4.36
4.72
Content Richness
4.53
4.59
4.80
4.25
4.50
4.71
4.31
4.59
4,70
4.49
4.51
4.78
Average
4.27
4.44
4.56
4.26
4.36
4.54
4.29
4.44
4.53
4.27
4.36
4.57
F.10 RESULTS IDGG ON GRAPH EMBEDDING METRICS
Full results on the graph embedding metric under IDGG are provided in Table 52 exhibiting the importance of incorporating structural and textual information in DyTAG generation.
Table 52: The results on the graph embedding metric under IDGG.
DeepSeek w/ M.
Llama w/ M.
Qwen w/ M.
GPT
w/o M.
w/ M.R.
w/o M.
w/ M.R.
w/o M.
w/ M.R.
w/o M.
w/ M.
w/ M.R.
Sephora
0.621
0.661
0.634
0.602
0.612
0.647
0.569
0.587
0.616
0.601
0.603
0.628
Dianping
0.739
0.749
0.769
0.426
0.457
0.531
0.542
0.521
0.527
0.505
WikiRevision
0.682
0.699
0.533
0.578
0.621
0.614
0.629
WikiLife
0.542
0.555
0.804
0.501
0.518
0.536
0.541
0.553
0.549
0.510
0.534
0.531
IMDB
0.601
0.616
0.729
0.511
0.522
0.568
0.502
0.557
0.588
0.521
0.535
0.563
WeiboTech
0.591
0.562
0.601
0.531
0.501
0.514
0.536
WaiboDaily
0.541
0.567
0.555
0.620
0.623
0.638
0.498
0.51.
0.503
0.459
0.481
0.502
Corn
0.610
0.623
0,828
0,655
0.642
0.635
0.526
0.599
0.634
0.541
0.552
0.572
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F.11 RESULTS OF VRDAG, DG-GEN, AND TIGGER-I
Comprehensive results on the graph structural and the graph embedding metrics under IDGG of GAG-General VRDAG, DG-Gen, and TIGGER-I are shown in Table 53 Since VRDAG (Li et al. 2024), DG-Gen (Hosseini ct al. 2024), and TIGGER-I (Gupta et al. 2022) do not support text generation, textual quality comparisons are not feasible. Furthermore, node/edge representations generated by VRDAG and DG-Gen are directly used as node/edge features when computing graph embeddings. The experimental results demonstrate that GAG-General significantly outperforms VRDAG, DG-Gen, and TIGGER-I in both graph structural quality and graph embedding quality. Notably. VRDAG and TIGGER-I struggles to generate high-qu sigh-quality datasets across all eight GDGB datasets, while DG-Gen achieves marginally better performance due to its explicit control over the size of generated graphs. These findings highlight the limitations of existing dynamic graph generation models in the DyTAG generation task and underscore the need for future models to effectively balance structural fidelity, temporal dynamics, and textual richness.
Table 53: The results the graph structural and the graph embedding metrics under IDGG of our framework and current feature- reature-supportive dynamic graph generation models. Ours correspond to the best results of our proposed GAG-General among four LLM backbones.
Datasets
Models
Ours
VRDAG
DG-Gen
TIGGER-I
Degree MMD J
0.370
0.795
0.422
0.622
Spectra MMD
0.189
0.847
0.274
0.687
Sephora
Power-law Validity
x
X
X 0,085
Graph Embedding
0.661
0,011
0.228
Degree MMD
0.150
0.887
0.167
0,446
Spectra MMD
0.351
0,808
0.245
0,341 / 0.580
Dianping
Power-law Validity
X
/
Graph Embedding
0.769
0.024
0.517
Degree MMD
0.102
0.807
0.197
0.658
WikiRevision
Spectra MMD
0.105
0.776
0.159
0.606
Power-law Validity
X
/
x 0.105
Graph Embeedinn
0.699
0.089
0.121
Degrec MMD
0.078
0.464
0.283
0.418
Spectra MMD
0.206
0.236
0.288
0.254
WikiLife
Power-law Validity
X
x
X 0.156
Graph Embedding
0.804
0.088
0.303
Degree MMD
0.145
0.882
0.373
0.679
Spectra MMD
0.379
0.710
0.537
0.653
IMDB
Power-law Validity
X
Graph Embedding
0.729
0.095
0.293
0.159
Degrec MMD
0.212
0.867
0,211
0.313
Spectra MMD
0.172
0.778
0.311
0.384
WeihoTech
Power-law Validity
X
/
Graph Embedding
0.695
0.085
0.292
0.289
Degree MMD
0.219
0.871
0.259
0.465
Spectra MMD
0.385
0.791
0.553
0.589
WeiboDaily
Power-law Validity
X
/
X
Graph Embedding
0.638
0.108
0.294
0.241
Degree MMD
0.073
0.877
0.212
0.372
Spectra MMD
0.181
0.760
0.365
0.389
Cora
Power-law Validity
X
X
X 0.197
Graph Embedding
0.828
0,053
0.056
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F.12 RESULTS OF IDGG ON UTILITY IN DATA AUGMENTATION FOR INDUCTIVE LEARNING
We further investigate the utility of DyTAGs generated under the IDGG task for data augmentation in inductive learning scenarios, which simulate real-world cold-start problems such as recommending new items users. We augment the training data of the original graph with the generated DyTAG (Aug.) and evaluate performance on an inductive link prediction task, where the goal is to predict future links involving nodes not observed during training. The DGNN models and the experimental settings that we used for evaluation are identical to those in Section F.5
As shown in Table 54] augmenting training data with DoGG-generated graphs consistently improves model performance across all datasets and models. The improvement in Average Precision (A AP) ranges from 0.027 108, with particularly notable gains on WikiLife and Cora. For example, CAWN achieves AAP of 0.108 on WikiLife, and DyGFormer shows a 0.046 improvement on Cora. These results demonstrate that IDGG-generated DyTAGs provide valuable structural and textual context for unseen nodes, effectively enriching the training signal and enhancing model generalization in cold-start settings. This validates the practical value of our generative framework in improving downstream model robustness through synthetic data augmentation.
1.13 VISUALIZATIONS OF THE HUB NODE STRUCTURES
The visualization of hub node structures in both the ground-truth and generated graphs from IDGG is presented in Figures 10 and II The top-3 hub product nodes highlighted in dark red. As described in Section 5.3 both graphs exhibit similar hub node patterns, yet the generated graph contains hub nodes formed through inductive generation, featuring entirely distinet textual profiles.
This divergence arises because IDGG emulates real-world graph evolution dynamics, preserving structural fidelity while generating new nodes with plausible attributes that align with the underlying generative mechanisms of real systems. For example, in recommendation systems, hub nodes in the generated DyTAG may represent emerging products with high virality potential, whereas those in the ground-truth graph correspond to established bestsellers. This capability establishes DyTAG generation as strategic tool for proactive decision-main makinn in e-commerce and digital marketing. By identifying potential future hubs, platforms can prioritize resource allocation for product promotion, optimize advertising strategies, and anticipate market trends before they emerge real-world data.
F.14 SEMANTIC-DRIE ANALYSIS FOR IDGG EVALUATION
To assess the long- terg semantic consistency of generated DyTAGs in the IDGG task, we introduce a cross- snapshot semantic drift detection evaluation. Specifically, we partition the generated edge DyTAG into (G each containing 1K newly added edges and corresponding newly generated nodes. For each snapshot Gr, we compute a global semantic representation h; by encoding all textual node and edge attributes using DERT-baae--nnaase Devlin et al. 2019) and aggregating via mean pooling:
10 temporal snapshots {G, t=1:
h
Σ (B.e(t) + Σ (etT(())txt))
Vol + |
LEV.
3
where V/ and &, denote the sets of nodes and edges introduced in the current snapshot t. We then measure inter-temporal semantic stability via cosine similarity between consecutive representations:
cos( het1), t= 1,...,9.
(2)
Thus, higher s, indicates greater semantic coherence over time.
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Table 54: The results link prediction (inductive) performance with data augmentation from the graphs generated from IDGG.
Model
Dataset
Type
Average Precision
AAP
Aug.
0.673 1 0.010
Sephora
0.037
Oni.
0.636 + 0.002
Aug.
0.667 + 0.020
WikiLife
0.099
TGN
Oni
0.568 + 0.024
Aug.
0.552 + 0.030
Cora
0.034
Oni
+ 0.019
Aug.
0.741 + 0.022
WeihoTech
0.041
Oni.
0.700 + 0.027
Aug.
0.750 1. 0.014
Sephora
0.050
Oni.
+ 0.013
Aug.
0.741 1: 0.010
WikiLife
0.108
CAWN
1
Aug.
0.651 +: 0.017
Cora
0.043
0.010
Aug.
0.740 + 0.009
WeiboTTech
0.033
0.707 0.009
Aug.
0.710 + 0.006
Sephora
0.027
Ori.
+ 0.025
Aug.
0.696 + 0.004
WikilLiee
0.031
GraphMixer
Oni.
0.665 + 0.030
Aug.
+ 0.003
Cora
0.040
1 0.018
Aug.
0.720 + 0.006
WeiboToch
0.058
1 0.029
Aug.
0.691 + 0.009
Sephora
0.051
0.003
Aug.
0.734 + 0.022
WikiLife
0.039
DyGFormer
0.695 0.023
Aug.
0.599 + 0.019
Cora
0.046
0.553 0.020
Aug.
0.736 + 0.008
WeiboTeech
0.040
Oni
0.696 + 0.016
On the Sephora dataset, shown in Figure 12 BERT-based similarity metrics reveal gradual decline after 5K edges under MAAA-GGeneral--Sggettinng emerging semantic drift (e.g., generation of out-of-domain items like "makeup bag"). Importantly, we further analyze the semantic drift under guided generation. As shown in Figure 12] we find that the drift is significantly reduced when domain constraints (c.g., "only generate reviews of makeup products (edge generation)/core makeup products (node generation), which are possible be shown on Sephora platform") "" are incorporated into the prompts for edge generation (Table (59) or node generation (Table 60), demonstrating the effectiveness of guided generation future work.
F.15 HUMAN-AMENITY CHECK FOR IDGG EVALUATION
To evaluate the practical usability and naturalness of generated content, conduct small-scale human evaluation on the IDGG-generated DyTAGs. Five skilled annotators independently assessed 50 randomly sampled newly generated nodes and their interactions (e.g., reviews, revisions, citations, etc.). Each item
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Product ID: 211709 Name: Acme Control Clarifying Clenser
Brand: Murad
Seplorr
Scpksen Product Node
Scalorr Product Nade
CTop llab))
Figure 10: The visualization of the hub node structures in the ground-truth graph.
98
Product ID: 1eem62799 (Newly Generated) Name: Raliant Clarity Vitamia C.mmm
Brand: RadiantSkin
Septura
Sephors
Figure 11: The visualization of the hub node structures in the generated graph.
was rated on a 5-point Likert scale across three dimensions: naturalness, plausibility, and consistency with platform context.
The results shown in Table 55 For instance, the results Sephora with GPT as the LLM backbone show that generated nodes (users/products) achieve an average score of 3.9, while generated edges (c.g.. user reviews) receive higher average of 4.3. These scores indicate that the majority of generated content is perceived as realistic, contextually appropriate, and aligned with real-world user behavior on the target ceftfrrm-suppottinn the human amenity of our framework in practical deployment scenarios.
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General:
General: Qwen
Backbone GAG-General:
0.8
0.8
0.8
GAG-General two constraints)
GAG-General Jwls constraints)
GAG-General (w/ constrabits)
Similarhy
GAC Ceneral twl domain constraints)
Similarit
GAG General (wi domain constraints)
0EAT bexed Cosine Similerity
GAG General (wr domain constraints)
0.7
0.7
0.7
0.6
0.6
BERTbaed
0.4
eopes
BERT
0.4
0.4
SK edges
0.3
0.3
0.3
10
Generated
Generated Edges
Generated
Figure 12: Cross-snapshot semantic consistency over three L.M backbones in IDGG on Sephora.
Table 55: Human evaluation average scores (5-point Likert scale) for generated nodes and edges under IDGG across different LLM backbones and datasets. Higher scores indicate better naturalness, plausibility, and consistency.
DeepSeek Node
Llama
Qwen
GPT
Edge
Node
Edge
Node
Edge
Node
Edge
Sephora
3.7
4.2
3.6
4.1
3.8
4.3
3,9
4.3
Dianping
3.5
4.0
4.0
4.5
3,6
4.1
3.8
4.2
WikiRevision
3.3
3.7
3.2
3.6
3.6
4.0
3.5
3.9
WikiLife
3.6
4.1
3.5
4.0
3.7
4.2
3.8
4.3
IMDB
3.8
3.3
3.8
4.0
4.1
Weibo
3.2
3.6
3.1
3.5
3.3
3.7
3.4
3,8
WeihoDaily
3.3
3.8
3.8
4.3
3.4
3.9
3.5
4.0
Cora
3.5
4.0
3.9
4.5
3.6
4.1
3.7
4.2
G PROMPTS
G.1 PROMPT TEMPLATES OF BIPARTITE GRAPHS
Table 56: The prompt template for node description in bipartite graphs (e.g. Sephora).
Node (Sephora User): Author ID: (node_id) id), Skin Tone: (skin [one] Eye Color: reye_color],
Skin Type: [skin_tyy] ], Hair Color: [hair_ color), Total Negative Feedback Count: [to-
tal all_neg_at feedback ). Total Positive Feedback Count: (total_pos feeedloeeeeebekce___e_e count)
Node (Sephora Product): Product ID: [node _i) Product Name: {product_name, Brand Name:
[brand [ Primary Category: (primary-categon, category| Secondary Category: (secondary category) Ingredients of the Product: (ingredients) Loves Count the Product: (loves_ couunt Product Rating:
(rating) Number of the Product Reviews: [reviews) Size of the Product: (size) Product Price (USD): (price_usd)
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Table 57: The prompt template for edge description bipartite graphs (e.g. Sephora).
Edge (Sephora Review): Review Time (yyyy-mm-dd); (timestamp) Rating: [rating) } Re- view Title: (review_titee Review Text: freview _extt Total Negative Feedback Count: {to- tal neg feedback feedbackkcount Total Positive Feedback Count: (total pos feedback_ count)
Table 58: The prompt template for memory reflection in bipartite graphs (e.g. Sephora).
As customer of the Sephora online shopping platform, you can review Sephora products based on the provided information and your own situation: (node info]
Here are your previous review history: (node_ memory] Now, based on your personal description and past review history, progressively refine your memory into concise version, ensuring it reflects your personal preferences.
Respond.
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Table 59: The prompt template for edge generation in bipartite graphs (e.g. Sephora).
Query:
You are customer of the Sephora online shopping platform, you can review Sephora products based the provided information and your own situation: {node info)
Here's your past reviews history: cond___emor] FIRST, you should Search for candidate prodeuts using the provided tools. Respond.
Action:
You are customer of the Sephora online shopping platform, you can review Sephora products based on the provided information and your own situation: fnode_ing info)
Here's your past reviews history: (node_memory memory
Here's the candidate prodeuts you can review: [node_items)
Here's the example of how you should proceed with your review: (interaction _etrrioo__eammeee
You should review ONE product. You can review the chosen product with detailed text and rate it. Additionally, you should predict how many positive/negative feedbacks will be received for this review. The predicted time of the review should be firmly related to the time in your past reviews history (relatively later than equal to them). Respond using the following detailed JSON format for ONE product:
"review":
item id: (str, "The ID of the product you want to review. Be sure be one of the Item IDs mentioned above!"),
timestamp: (str, "The time of review (yyyy-mm-dd)",
rating: (int, "The overall rating given to the product (From to 5)"),
review_titke (str, "The title of your review"),
review_text (str, "Your detailed review text"). total lotal_neg__ lal_innggeeeeeee feedeack__oooooee count: (int, "Number negative feedback received for this review"), total pos fital_pos_feedlee feedback count: (int, "Number of positive feedback received for this review")
Respond.
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Table 60: The prompt template for node generation in bipartite graphs (e.g. Sephora).
Node (Sephora Author):
Now we have a Sephora dataset, which records Sephora users' reviews on several Sephora products. Here's 's information of the recent active Sephora author nodes: (recent node info You are expected to generate ONE new Sephora author node for the Sephora dataset, and ensure that the generated new node is somewhat different from the existing nodes. Respond using the following detailed JSON format for ONE new Sephora author:
"sephora_ au
node id: (str, "The the generated author (Format: G + 6-digit random number)"), node_type type: sephora author, skin_tone: (str, "The skin tone of the generated author (e.g. light, fair, medium Tan, tan, olive, etc.)")
eye_ _oc_color: (str, "The eye color of the generated author (e.g. brown, green, hazel, blue, etc.)"), skin_type: (str, "The skin type of the generated author (e.g. oily, dry, combination, normal, etc.)").
coirrc (str, "The hair color of the generated author (e.g. brown, black, blonde, etc.)"),
auburn,
total nng @aal_nee_feedbaleeeee feedback count: (int, "The number of total negative feedback received from other authors of the generated author"),
total pos lis__ooseeeee feedback count: (int, "The number of total active feedback received from other authors of the generated author"),
Respond.
Node (Sephora Product):
Now we have a Sephora dataset, which records Sephora users' reviews on several Sephora products. Here's information of the recent active Sephora product nodes: frecent node info)
You are expected to generate ONE new Sephora product node for the Sephora dataset, and ensure that the generated new node is somewhat different from the existing nodes. Respond using the following detailed JSON format for ONE new Sephora product:
"sephora_prodee producttp node id: (str, "The node_tyye sephora_ ppoducct
the generated product (Format: 5-digit random number)"),
podduct_names (str, "The name of the generated product"), brand_name: (str, "The name of the brand of the generated product"), primary_category: (str, "The primary category the generated product"), secondaryycategorry:
category: (str, "The secondary category of the generated product"),
ingredients: (str, "The ingredients of the generated product"),
loves count: (int, "The loves count from the users of the generated product"), rating: (float, "The avg rating from the users of the generated product"), reviews: (int, "The number reviews from the users of the generated product"), size: (str, "The size the generated product"). price_usd: (float, "The price the generated product"),
Respond.
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G.2 PROMPT TEMPLATES OF NON-BIPARTITE GRAPHS
Table 61: The prompt template for node description in non-bipartite graphs (e.g. Weiho'Tech).
Node (Weibo User): User ID: {node_in j User Name: {user_name| User Source: fuser_ source] User Gender: (user_gender) User Location: (user loca_ooccion)) Number of the User's Follow- ers: saser_followers) Number of the User's Followees: (user_friende User Description: (user_description)
Table 62: The prompt template for edge description in non non- bipaariat graphs (e.g. WeiboTech).
Edge (Weibo Interact): Interaction Time (yyyy-mm mm-dd hh-mm--s): (timestamp) Interaction Type:
(label) Source User Text: (sre_text) Destination User Text: [dst_text)
Table 63: The prompt template for memory reflection in non-bipaet graphs (e.g. WeiboTech).
As a Weibo user, can search for other Weibo users who may interact with you on the online social media Weibo platform: (node info)
Here are your previous interactions history: (node_memory. Now, based on your personal description and past interactions history, progressively refine your memory into concise version, ensuring it reflects your personal preferences. Respond.
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Table 64: The prompt template for edge generation in non-bipartite graphs (e.g. WeiboTech)..
Query:
As a Weibo user, you need to search for other Weibo users who may interact with you on the online social media Weibo platform: [node. info)
Here are your previous interactions history: mrode_memon First, utilize the provided tools to search for potential Weibo users who may interact with you. Respond.
Action:
As a Weibo user, you need to search for other Weibo users who may interact with you on the online social media Weibo platform: (node info)
Here are the potential Weibo users you can choose from: [node i
Here your previous interaction history: [node_memors
Here's the example of how to interact with others: inreractiio_examppe
You should select ONE destination user, you re tend to select the one you have interacted with before. Respond using the following detailed JSON format:
"interact"::
item_id: (str, "The ID of the destination user. Be sure to be one of the Item IDs mentioned above!")
Respond.
Request:
As a Weibo user, you need to search for other Weibo users who may interact with you on the online social media Weibo platform: (node_info)
Here is your previous interaction history: [node_mg [onde_memor]]
The chosen destination Weibo user who may interact with you is: (item info
Here's the interaction history of this chosen Weibo user: (item memory)
Here's s the example of how to interact with others: finteraction _exoppleee_m You should select ONE destination user. You should post texts the source user and the selected destination user should interact with you in detailed text. Additionally, you should label the type of the interaction (comment repost). The predicted time of the interaction should be firmly related the time in your previous interaction history (relatively later than or equal to them). Respond using the following detailed JSON format:
"interact":f
item_id: (str, "The ID of the destination user"), timestamp: (str, "The time of the interaction (yyyy-mm-dd hh-mm-ss)"), label: (str, "The type of interaction (TWO TYPE: I.comeett 2.repost)"), sre text: (str, "The text from the source user"). det_text: (str, "The text from the destination user")
Respond.
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Published as conference paper ICLR 2026
Table 65: The prompt template for node generation in non-bipartite graphs (c.g. WeiboTech).
Node (Weibo User):
Now we have a weibo dataset, which records the interaction history between Weibo users. Here's s information of the recent active user nodes: (recent node info)
You are expected to generate ONE new user node for the weibo dataset, and ensure that the generated node is somewhat different from the existing nodes. Respond using the following detailed JSON format for ONE new user:
"weibo_used user":f
node id: (str, "The of the generated user (Format: G 5-digit random number)"), mode_typed weibo_ user,
user_name: (str, "The name of the generated user"),
user_source: (str, "The souree((Plocttion/devve) of the generated user"), user gender: (str, "The gender of the generated user"),
user location: (str, "The location of the generated user"),
user user_followers: (int, "The number of the followers of the generated user"), user frieeds: (int, "The number of the followees of the generated user"), user_description: (int, "The description of the generated user"),
Respond.
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Published as conference paper ICLR 2026
G.3 PROMPT TEMPLATES OF LLM-AS-EVALUMAE IN TEXTUAL QUALITY METRICS
Table 66: The prompt template of evaluation criteria for L.Maas-Bvvluutorr
Please evaluate the role-playing ability of the ACTOR NODE based on its actions with ITEM NODES across multiple turns based on scene, ACTOR NODE information, action history and evaluation criteria.
[Environment Description]: (environment)
[ACTOR NODE Description]: (actor (actor_node)
[Multi-tum Actions]: (actions)
Strict Evaluation Criteria:
1. Factual Accuracy: Identify and point out any elements that do not accurately match the historical factual backdrop.
2. Behavior Consistency: Explicitly highlight inconsistencies between the actor nodes' actions and their traits.
3. Logical Coherence: Point out any logical fallacies or actions that contradict the established context
or logic.
4. Content Redundancy: Identify repetitions in actions that could detract from engagement and realism.
Assess whether emotional responses and expressions appropriate and convincingly portrayed, highlighting any discrepancies.
5. Emotional Expression:
6. Interaction Adaptability: Critique the actor node's interactions with item nodes, noting any unnatural or contextually inappropriate responses.
7. Creativity and Originality: Evaluate the creativity of responses and actions, pointing out generic or unoriginal content.
Detail Handling: Scrutinize the level of detail in scene setting and actor node enactment, marking areas lacking depth or accuracy.
Style Consistency: Ensure that the linguistic style remains consistent, identifying any deviations.
10. Fluency and Quality: Critically assess the smoothness and quality of the text, highlighting any grammatical errors or awkward phrasings.
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Published as conference paper ICLR 2026
Table 67: The prompt template of scoring criteria for LaMM----Evaluator
[Scoring Criteria]:
1. Contextual Fidelity:
Responses are often incorrect irrelevant, significantly inconsistent with the actor node's s background.
Point:
-3 Points:
Responses generally accurate, though there occasional errors some details not very relevant to the actor node's background.
Responses are always accurate and highly relevant to the actor node's historical or professional background, demonstrating deep knowledge and skills.
5 Points:
2. Personality Depth:
Point: The displayed personality traits often conflict with lack consistency with the actor node's setup.
Points:
Personality traits generally match the actor node's setup, though there are occasional
inconsistencies.
Points: Consistently displays behavior and language choices that match the core personality traits of the actor node, showcasing the actor node's uniqueness.
Dynamic Adaptability:
Point: The actor node struggles to adapt to new scenarios over time, producing rigid or illogical responses.
3 Points: Adaptation occurs in most situations but occasionally falters in handling unexpected turns maintaining coherence.
The actor node fluidly adapts to novel contexts and introduces innovative solutions and maintains consistency even under challenging conditions.
Points:
4. Immersive Quality:
Point: Actor node portrayal is often inconsistent, making it difficult for users to immerse
understand the actor node.
-3 Points: Actor node is mostly consistent, but occasional contradictions slightly affect immersion.
Points: Actor node portrayal always consistent, enhancing user immersion and effectively showing self-awareness and actor node limitations.
Content Richness:
1 Point: Output is sparse, lacking depth, detail, or meaningful interaction with item nodes.
Content is adequate but occasionally superficial, missing opportunities for nuanced or layered interactions.
Points:
5 Points: Output exceptionally dense with relevant details, creative ideas, and meaningful engagement with item nodes. It showcases high level of expertise and creativity, providing users with an abundance of valuable information and interactive elements that significantly enhance the overall experience.
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Published as conference paper ICLR 2026
Table 68: The prompt template of evaluation steps and response formats for as- as---aluatorrr.
[Evaluation Steps]:
1. Contextual Understanding: Examine the actor node's profile and background information thor- oughly to fully grasp the nuances of their context, motivations, and historical background. Behavioral Observation: Monitor how the actor node reacts across different scenarios, paying special attention their decisions and interactions.
Criteria-Ban Basid Assessment: Strictly analyze each observed behavior using the above criteria to systematically evaluate the consistency and effectiveness of the actor node's portrayal.
Your response must follow the format provided below. Please note that only when the content quality is extremely good can 5 Points be given.
[Response Format]:
Contextual Fidelity: [1-5]
Personality Depth: [1-5]
Dynamic Adaptability: [1-5]
Immersive Quality: [1-5]
Content Richness: [1-5]
[Response Format Example]:
Contextual Fidelity: 3
Personality Depth: 3
Dynamic Adaptability:
Immersive Quality: 3
Content Richness:
[Response]:
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General Description: This a dataset recording users' reviews of products on the Sephora e-commerce platform, including information about each user's 's age, skin type, eye color, information about each product, and users' reviews of each product. Main Actors: The users who review products on the review various products, Published as conference paper ICLR 2026
Table 69: The prompt template of dataset descriptions for
Sephora:
Sephora e-commerce platform. Main Activities of Actors: Sephora rating products, and providing additional user experiences.
Dianping:
General Description: This is dataset recording users' reviews of businesses on the Dianping platform, including information about each user, information about each business, and users' reviews each business. Main Actors: The users who review businesses on the Dianping platform. Main Activities of Actors: Dianping users review various businesses, rating businesses, and providing additional user consumption experiences.
WikiRevision:
This is dataset recording users' revisions Wikipedia pages, including information about each wiki page and comments submitted by users during revision. Main Actors: The users who revise the wiki pages. Main Activities of Actors: Wiki users revise various wiki pages (correcting errors in wiki pages, adding new content, removing redundant content, and summarizing the wiki pages) with a comment brief summary in text.
General Description:
WikiLife:
General Description: This a dataset recording the life trajectories of several people (celebrities), including basic information about each person, information about the location of each life trajectory, and the events happened at those locations for each person. Main Actors: The people who physically present in some locations for various categories of life trajectories. Main Activities of Actors: People (celebrities) physically present in the specified location for various life trajectories, such as being born, studying, working, getting married, having children, and passing away.
IMDB:
General Description: This is dataset recording the collaboration relationships of movie ac- tors/actresses, including information about each movie actor/actress, information about the movies they collaborated on, and the roles they played in those movies. Main Actors: The movie ac- tors/actresses who collaborate with each other. Main Activities of Actors: Movie actors/actresses collaborate with others and playing different roles movies.
WeihhTTech/ WeibooTech//iiiDDalill WeiboDaily:
General Description: This is dataset recording users' interaction on the Weibo platform, including information about each user and the text information attached during user interactions such as comments and reposts. Main Actors: The users who interact with each other on the Weibo platform. Main Activities of Actors: Weibo users make multiple posts, and other users interacting with them by commenting or reposting the posts.
Cora:
General Description: This is a dataset recording paper citation relationships. including information about each paper and the specific text attached during citation in specific section of the paper. Main Actors: The academic papers which cite other related papers. Main Activities of Actors: Academic papers cite other related papers with specific citation sentences in the text.
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Published as a conference paper ICLR 2026
H USE OF LARGE LANGUAGE MODELS (LLMS)
LLMs were used in two specific aspects of this work. First, we employ LLMs to polish the manuscript text and refine figure captions for clarity and presentation quality. Second, our proposed generative framework, GAG-General, is built upon LLM-based multi-agent architecture that coordinates multiple specialized agents to generate structurally and semantically coherent DyTAGs. The LLMs this framework are used to model textual attributes and support agent reasoning during graph generation. No other parts of the research, including problem formulation, method design, or analysis, involved significant LLM assistance.
63


Reviews:
[{"review_id": "HacOszioyJ", "reviewer": "Reviewer_iU4e", "summary": "GDGB presents a generative Dynamic Text-Attributed Graph benchmark with eight text-rich datasets, two tasks (transductive and inductive DyTAG generation), and metrics covering structure, time, and text. An LLM-based multi-agent framework, GAG-General, standardizes evaluation and delivers competitive results. Experiments show that high-quality text and structure are crucial, enabling rigorous assessment.", "questions": "All my major and minor concerns are mixed in the Weaknesses part. Please kindly refer to it.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 4, "contribution": 2, "strengths": "1. The paper is well written and easy to follow.\n\n2. The authors introduce several dynamic text-attributed graph datasets with higher text quality than existing benchmarks.\n\n3. The authors design an LLM-based multi-agent generative framework; an accompanying illustration would improve comprehensibility.", "weaknesses": "### **Major Concern**\n\n1. **Claim of the “first” generative DyTAG benchmark.**\n   The paper claims to be the first generative DyTAG benchmark, yet it does not evaluate **existing TAG-generation or DyG-generation methods**. Instead, it varies only the LLM backbones for DyTAG generation. Without comparisons to prior generative baselines, the “first” or “state-of-the-art” claim is overstated. Please include representative TAG/DyG generative methods (or strong non-LLM baselines) and report side-by-side results.\n\n2. **Assessment of text quality and incomplete reporting on DTGB.**\n   The paper asserts that existing DyTAG datasets have poor text quality; however, the reported results on DTGB appear strong in both graph-embedding metrics and textual quality scores. Moreover, only **partial** DTGB results are shown.\n\n   * Please explain the selection criteria for the reported DTGB subsets.\n   * Provide complete results on all DTGB datasets for both generation tasks (transductive and inductive), using the same metrics and settings.\n\n3. **Direct comparison between the new datasets and existing ones.**\n   To establish the value of the introduced datasets, the main paper should present **explicit empirical generation comparisons** between DTGB and the newly proposed datasets. This will make the dataset-quality argument far more convincing.\n\n4. **Metric definitions and justification.**\n   Evaluation is central in generative work, yet the three metrics are not formally defined. Please:\n\n   * Provide precise mathematical definitions (inputs, outputs, normalization, and aggregation).\n   * Justify why each metric is appropriate for DyTAG generation and discuss potential limitations or failure modes.\n   * Clarify implementation details\n\n\n### **Minor Concern**\n\n   Could you please elaborate on concrete use cases for dynamic text-attributed graph generation?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "nqUx0y6evD", "reviewer": "Reviewer_EtXQ", "summary": "GDGB introduces a high-quality benchmark for generative learning on dynamic text-attributed graphs (DyTAGs), accompanied by two novel tasks (TDGG and IDGG) and a multi-agent LLM framework (GAG-General). The datasets notably surpass prior ones in textual richness, and the holistic evaluation protocol covers structure, text, and time.", "questions": "N/A", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. This paper is well written and easy to follow.\n2. The paper proposes the 1st dedicated generative DyTAG benchmark with rich, realistic node/edge texts across eight diverse domains.\n3. The TDGG and IDGG tasks is well defined, and the metrics (structure, text, embedding) give a comprehensive quality picture.", "weaknesses": "1. IDGG new-node evaluation may lack direct semantic-drift or human-amenity checks.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "OugAixNWuD", "reviewer": "Reviewer_xhXC", "summary": "The paper highlights 2 key concerns with existing dynamic text-attributed graphs: a)Lack of high-quality textual attributes- either completely absent or limited to non-semantic quality, like only containing usernames or emails; b) Lack of standardized DyTAG generative tasks formulations and evaluation protocols: Existing evaluation frameworks don't incorporate textual characteristics.  To solve for this, the authors propose the following contributions\na) Eight DyTAG datasets covering diverse domains with rich textual semantic information\nb) Two novel tasks on these DyTAGs: Transductive dynamic graph generation and Inductive Dynamic graph generation with metrics converting Graph Structural Metrics, Textual Quality Metrics, and Graph Embedding Metrics. \nc) Baseline: Given this suite of datasets, the paper proposes GAG-General, which is an LLM-based multi-agent framework adopted for DyTAG generation tasks. \n\nCollectively, the paper proposes a robust benchmarking for DyTAG generation.", "questions": "Overall, the paper is of significant use to the temporal graph learning community, which is in dire need of benchmark datasets, especially in dynamic attributed graphs and a consistent evaluation framework.   But paper in this current form needs clear positioning and a more robust evaluation framework. \n\nI will be open to accepting if authors can address these concerns meaningfully.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "a) There is a clear gap in standardized datasets for textual attributed graphs, which this paper attempts to fill.\nb) Novel Eight text attributed dynamic graph datasets proposed\nc) Comprehensive comparison against existing datasets in terms of richness and utility of these textual attributes to highlight their importance\nd) The paper clearly motivates the problem and reports a detailed analysis of these datasets.", "weaknesses": "A) The paper somewhat dilutes its core contribution by introducing a Generative framework: GAG-General, which is adopted from existing work. Core contribution could only be a textual attributed benchmark with evaluation metrics. And this generative framework could be proposed as a baseline method, presenting it as a central contribution, overemphasizes an incremental component, as novelty here is very limited. \n\nB) Text quality and graph embedding metrics: There seems to be a dependency on the underlying LLM  on final performance. However, the paper provides no clear guidance on which model should be considered standard. Can the author guide which LLM should be used for evaluation, or can any one of them be used? The evaluation framework needs to be final, but the proposed one seems to be dependent on the underlying LLM, with no clarity on which to use.  There is a table 3 in the experiment section, but it's not clear what the conclusion is from it. \n\nAn evaluation framework that is dependent on underlying LLMs will lead to different researchers obtaining inconsistent results. Any new LLM version update or slight change in prompt can lead to different results. The authors of this paper should define a fixed or recommended LLM backbone with recommended hyperparameters during LLM inference and input for consistency, robustness, or a way to minimize the LLM-specific biases.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)