Global-Recent Semantic Reasoning on Dynamic Text-Attributed Graphs with Large Language Models
Authors: Yunan Wang
Jianxin Li
Ziwei Zhang
Venue: ICLR 2026 Poster (2026)
Keywords: Dynamic text-attributed graph
graph learning
large language model
Primary area: learning on graphs and other geometries & topologies
TL;DR: This paper propose DyGRASP, which captures the recent-global semantics inherent in dynamic text-attribute graphs with large language models.

Abstract: Dynamic Text-Attribute Graphs (DyTAGs), characterized by time-evolving graph interactions and associated text attributes, are prevalent in real-world applications. Existing methods, such as Graph Neural Networks (GNNs) and Large Language Models (LLMs), mostly focus on static TAGs. Extending these existing methods to DyTAGs is challenging as they largely neglect the *recent-global temporal semantics*: the recent semantic dependencies among interaction texts and the global semantic evolution of nodes over time. Furthermore, applying LLMs to the abundant and evolving text in DyTAGs faces efficiency issues. To tackle these challenges, we propose $\underline{Dy}$namic $\underline{G}$lobal-$\underline{R}$ecent $\underline{A}$daptive $\underline{S}$emantic $\underline{P}$rocessing (DyGRASP), a novel method that leverages LLMs and temporal GNNs to efficiently and effectively reason on DyTAGs. Specifically, we first design a node-centric implicit reasoning method together with a sliding window mechanism to efficiently capture recent temporal semantics. In addition, to capture global semantic dynamics of nodes, we leverage explicit reasoning with tailored prompts and an RNN-like chain structure to infer long-term semantics. Lastly, we intricately integrate the recent and global temporal semantics as well as the dynamic graph structural information using updating and merging layers. Extensive experiments on DyTAG benchmarks demonstrate DyGRASP's superiority, achieving up to 34\% improvement in Hit@10 for destination node retrieval task. Besides, DyGRASP exhibits strong generalization across different temporal GNNs and LLMs.

Published as a conference paper at ICLR 2026
GLOBAL-RECENT SEMANTIC REASONING ON DY- NAMIC GUAGE MODELS
GRAPHS WITH LARGE LAN-
Yunan Wang, Jianxin Li, Ziwei Zhang'
School of Computer Science and Engineering, Beihang University zwzhangebuaa. edw.aa.aaddc..... cn
ABSTRACT
Dynamic Text-Atrribute Graphs (DyTAGs), characterized by time-evolving graph interactions and associated text attributes, are prevalent in real-world applica- tions. Existing methods, such as Graph Neural Networks (GNNs) and Large Language Models (LLMs),, mostly focus on static TAGs. Extending these exist- ing methods to DyTAGs is challenging temporal semantics: the recent semantic dependencies among interaction texts and the global semantic evolution of nodes over time. Furthermore, applying LLMs to the abundant and evolving text in DyTAGs faces efficiency issues. To propose Dynamic Global-Recen Adaptive Semantic
they largely neglect the recent-global
tackle these challenges, Processing (DyGRASP). a novel method that leverages LLMs and temporal GNNs efficiently and effectively reason DyTAGs. Specifically, we first design a node-centric implicit reasoning method together with a sliding window mechanism efficiently capture recent temporal semantics. In addition, capture global semantic dynamics of nodes, we leverage explicit reasoning with tailored prompts and an RNN-like chain structure to infer long-term semantics. Lastly, we intricately integrate the recent and global temporal semantics as well as the dynamic graph structural information using updating and merging layers. Extensive experiments DyTAG benchmarks demonstrate DyGRASP's superiority, achieving up to 34% improvement Hit @ for destination node retrieval task. Besides, DyGRASP ex- hibits strong generalization across different temporal GNNs and LLMs. Code available at ht tttps: github. com/ Nala- YN/DYGRASP.
INTRODUCTION
Dynamic Text-Attribute Graphs (DyTAGs) are widely present in real-world scenarios like E- commerce, knowledge graphs, and social networks (Khrabrov & Cybenko, 2010; Deng et al., 2019; Song et al., 2019; Zhang et al., 2022; Tang et al., 2023; Luo et al., 2023; Huang et al., 2022). Unlike commonly studied TAGs (Sen et al., 2008; Giles al., 1998; Mernyei & Cangea, 2020; Hu et al., 2020; He et al., 2023; Yan et al., 2023), where nodes only contain static text attributes, DyTAGs contain evolving information over time and involve interactions accompanied by timestamp and dynamic text attributes (Zhang et al., 2024). For example, consider an E-comment graph illustrated in Figure where nodes represent users or merchants and interactions represent user reviews to merchants. Clearly, the spatio-temporal patterns of Dy TAGS contain more abundant information than static TAGs. How to fully mine the rich value underlying DyTAGs essential for both academia and industry.
For static TAGs, most primitive approaches resort to Graph Neural Networks (GNNs) (Wu et al., 2019; Li et al., 2021; Veličković et al., 2018; Hamilton et al., 2017; Kipf & Welling, 2017) for their end-to-end learning capabilitics. While excelling at capturing graph structural information, these methods usually only adopt shallow text encodings, such Bag-of-Words Words word embeddings as features, thus lacking strong semantic knowledge to understand the textual attributes comprehensively. Recently, Large Language Models (LLMs) (Abdin al., 2024; Almazrouei al., 2023; Touvron et al.,
"Corresponding Author.

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LLMs and GNNs for Static TAGs
Prompt
Statio Feature
Static Explanation
LLM
GNN
Generate
stattic
Dynamic Semantic Processing on DyTAG
Sequence
lg
14
Cesual Sequence
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00 LET
Addiess: Cescciption-..
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An example of DyTAG
Global Dynamic Capture
Figure 1: An illustrative comparison between handling static TAGs and DyTAGs. Static methods that utilize LLMs usually generate static embeddings or explanations for text attributes. In comparison, DyTAGs contain rich spatio-temporal information, such as recent semantic dependency or global semantic dynamics of nodes, which the existing methods for TAGs do not account for.
2023; Guo et al., 2025; Achiam et al., 2023), pretrained on vast text corpora, have demonstrated strong capabilities in text understanding and generation. Thus, researchers have explored various methods to combine GNNs and LLMS for TAGs (Zhu et al., 2025; Wang et al., 2024b; Khoshraftar et al., 2025; Beiranvand & Vahidipour, 2025; Chen et al., 2024), aiming to integrate textual information into graph structures. Despite the remarkable progress in combining LLMs and GNNs for TAGs, these methods cannot be directly transferred to DyTAGs due to the following challenges:
Firstly, existing GNNs and LLMs neglect the global-recent semantic dynamics in DyTAGs. In real life, the temporal changes of many phenomena conform to complicated mixture of recent and global patterns. For example, ice cream sales might be related to recent promotional activities while also being influenced by long-term dictary structure changes. Specifically, we investigate two complementary temporal semantic features with different temporal granularities within DyTAG:
The text attribute of an interaction in DyTAG exhibits recent semantic dependency. For instance, the word "notebook" might mean "notepad" following "visit bookstore" interaction, but the same word is more likely to mean "computer" after a "'isit electronics store" interaction.
Recent semantic dependency of temporal interactions:
Global semantic dynamics of nodes: Unlike static node semantics, node features in DyTAG also continuously undergo global changes, which are reflected by emerging interactions. For example, user in an E-commerce network may shift her interests from literature to technology. as reflected in visiting different kinds of shops.
Current GNNs and LLMs methods for TAGs largely ignore the above issues as they only employ static methods to model the semantic and structural information..
Secondly, DyTAGs pose more severe efficiency challenges for LLMs. For static TAGs, most text attributes are associated with nodes. However, for DyTAGs, there exist edge-level text (such as the example in Figure 1) and the edge attributes can also evolve with time. Considering that the number of edges is usually orders of magnitude larger than the number of nodes and there exist least hundreds or thousands time stamps, developing effective models for DyTAGs demands highly efficient LLM reasoning designs reduce computational costs.
To tackle these challenges, in this paper, we propose DyGRASP(Dynamic Global-Recceo Adaptive Semantic Processing), which leverages both the implicit and the explicit reasoning capabilities of LLM to capture recent-globe temporal semantics in DATSS Specifically, DyGRASP features three novel designs: (i) Implicit reasoning for recent temporal semanties: capture recent semantic dependency
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while retaining low computational complexity, we leverage the unidirectional consistency between temporal sequences and causal sequences of LLMs to achieve node-centric implicit reasoning. In addition, propose sliding window mechanism to organize node's ' historical interactions chronologically in batches for the LLM. Our proposed method models recent semantic features for multiple interactions simultaneously while ensuring future information leakage, which significantly reduces the number of consumed tokens so that our method can be scaled to real-world DyTAGs; (ii) Explicit reasoning for global temporal semanties: to learn node representations from extensive historical interactions while reducing consumed number of tokens, we utilize the generative reasoning ability of LLM and segment a node's historical interactions into constant number of partitions based on timestamps. Then, we carefully design prompts to instruct the LLM to summarize node's feature description for each period. Finally, we employ an RNN-like reasoning chain structure to pass long-distance semantics and dynamically update node features; (iii) Integrating Semantics and Graph Structure: lastly, we design three tailored layers to update recent semantics, global semantics, and graph structural features and then integrate them, together with a temporal GNN to learn comprehensive node representations.
To verify DyGRASP's effectiveness, conduct extensive experiments DyTAGs benchmark. Our proposed method achieves up to a 34% improvement in Hit@ 10 for destination node retrieval task compared to state-of-the-art methods. Besides, DyGRASP demonstrates strong generalization across different L. and Temporal GNNs. We also perform detailed analyses for different components and hyperparameters. Our contributions are summarized as follows:
We study the recent-global spatio-temporal patterns in DyTAGs, which are overlooked in existing GNNs and L.Mss for static TAGs.
We propose to leverage the implicit and explicit reasoning capabilities of LLMs to capture the recent-global semanties DyTAGs. Motivated by this goal, we propose DyGRASP, a tailored model fusing the advantages of LLMs and temporal GNNs.
We theoretically prove that our proposed method has optimized reasoning efficiency compared to straightforward methods, thereby reducing the cost of practical applications.
We verify the effectiveness of DyGRASP through extensive experiments on DyTAG benchmarks, outperforming tha baselines up to 34%.
2 RELATED WORK
LLM for TAGs. While GNNs excel at encoding graph structure features, preliminary methods exhibit deficiencies in understanding the textual content within TAGs. Consequently, researchers leverage the powerful capability of LLMs on text understanding to enhance the performance of GNNs on TAGs (Chien et al.: Yang et al., 2021; Xue et al., 2023; Wu et al., 2024; Wei et al., 2024; Guo et al., 2024; Liu et al., 2024). Among them, ENGINE (Zhu et al., 2024) employs LLMs to enhance the quality of node representations by proposing "G-Ladder" structure. TAPE (He et al., 2024) utilizes "explanations" generated by an LLM for the textual attributes as features. SimTeG (Duan et al., 2023) boosts textual graph learning by first fine-tume teeettnnigg& a language model to generate node embeddings, which are then used features for separate GNN. GraphGPT (Tang et al., 2024) employs graph instruction tuning to integrate I.LMs with graph structural knowledge. However, the aforementioned methods primarily target TAGs containing only static semantic features. The dynamic characteristies of DyTAGs render these approaches incapable of modeling the temporal semantic relationships therein. In contrast, we investigate the temporal semantics inherent in DyTAGs by leveraging the reasoning capabilities of LLMs.
Temporal Graph Neural Networks. To extend the capabilities of GNNs dynamic graphs, recent studies have proposed various temporal GNNs (Rossi et al., 2020; Poursafaci et al., 2022; Wang et al., 2021b; Luo & Li, 2022; Ma et al., 2020; Cong al., 2023; Xu et al., 2025; Liu al., 2025a;b) to model dynamic graph features. Among these, TGAT (Xu et al., 2020) introduces functional time encoding based on Bochner's thcorem. CAWN (Wang et al., 2021c) leverages temporal random walks to inductively learn representations of temporal network dynamics. DyRep (Trivedi et al., 2019) learns dynamic graph representations by modeling a latent mediation process between topological evolution and node activities. DyGFormer (Yu et al., 2023) learns solely from the historical first-hop interaction sequences of nodes to simplify the dynamic graph learning task. CTAN (Gravina et al., 2024) captures long-range dependencies in continuous-timg dynamic graphs via non-dissipative ODEs. TMetaNet
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(Li et al., 2025) leverages Dowker Zigzag Persistence guide meta-learning parameter updates for dynamic link prediction. Mspace (Rahman & Coon, 2025) models spatiotemporal dynamics using a baseed approach for node feature forecasting. However, the aforementioned temporal GNNs are unable to comprehend the textual semantic information within DyTAG, leading to suboptimal performance. In comparison, our method introduces LLMs reasoning over DyTAG to capture the temporal semantic features.
LLM for DyTAGs. Following the pioneering benchmark for DyTAGs named DGTB (Zhang et al., 2024), there has recently been few research on LLMs for DyTAGs, which are largely concurrent to our work. Among them, LKD4DYTAG (Roy al., 2025) distills knowledge from LLM into a temporal GNN. CROSS (Zhang et al., 2025) employs LLMs to dynamically extract text semantics and co-encoder to synergistically unify these semantics with evolving graph structures. GAD (Lei et al., 2025) utilizes multi-agent system with collaborative LLMs to directly perform prediction on DyTAGs without dataset-specific training. However, existing studies has not addressed the reasoning efficiency challenges when applying LLMs to DyTAG and are unable to identify the recent-global patterns inherent in DyTAG, thus failing to comprehensively capture its calliiggaaularity temporal semantic features.
NOTATIONS
Definition (DyTAG) A DyTAG is represented as = (V,,,, where V is the set of nodes, E CCVX x V is the set of edges, T is the set of observed timestamps, 'D is the set of node textual attributes, and R the set of edge textual attributes. The textual attribute of a node u denoted by interaction, occurring between nodes and timestamp t is represented as I = (u, r. U. t), where r E R represents the textual attribute.
Du D. An edge, also known
We use interaction to refer to an edge in DyTAGs to emphasize its dynamic nature, e.g., interaction text denotes the complete description of the source node's behavior towards the target node, rather than merely the edge's s text attribute itself. We use reasoning to represent the LLM's process of uncovering underlying information from a series of interactions. We aim to develop models that can handle various tasks associated with DyTAGs, such as node classification, link prediction, and destination node retrieval.
4 METHOD
In this section, we introduce our method. We first introduce implicit reasoning for recent temporal semantics in Section 4.1, then introduce explicit reasoning for global temporal semantics in Section 4.2, and finally integrate the temporal semantics with the dynamic graph structures in Section 4.3.
4.1 IMPLICIT REASONING FOR RECENT TEMPORAL SEMANTICS
Unlike the static attributes in normal TAGs, the textual attributes in DyTAG contain temporal semantic relationships with historical interactions. Therefore, we leverage the implicit reasoning ability of LLMs by harvesting the hidden embedding of texts within LLMs.
To obtain textual attribute embeddings that incorporate temporal semantics, an intuitive approach to take an edge-centric view and organize the textual attributes of an interaction and historical interactions chronologically. and use an LLM to extract the hidden embeddings for the interaction. However, denoting d as the average degree of the graph, the complexity (i.c., the number of consumed input tokens) of the LLM reaches O(EE) x d). For a real-world Dy DAG that has millions of edges and an average degree hundreds or thousands, this straightforward method suffers from an excessively high computational complexity. To address this potential issue while preserving temporal semantic relationships, we propose two key designs: (i) node-centric implicit reasoning. which leverages the unidirectional consistency between the causal order inherent in LLMs and the chronological order of the dynamic graph; (ii) sliding window mechanism, which segments interaction sequences into overlapping batches to balance computational consumption and semantic feature modeling capability. Using these two designs, our proposed method can theoretically reduce the computational complexity to O(E1) (Proof is shown in Appendix C). Next, we elaborate these two designs.
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(a) Implicit Reasoning for Recent Temporal Semantics
(c) Integrating Semantics and Graph Structure
Invisibie
MM
RccentSemante Layer
Herge Layar
[R(41
LLM
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Occoder (Cosual Attemior
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Recent Tomporal Semantics
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[b) Explicit Reasoning for Global Temporal Semantics
Layer
Merpe Layer
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Figure 2: An overview of DyGRASP. DyGRASP first utilizes the implicit and explicit reasoning capabilities the LLM to respectively extract recent and global temporal semantic features from DyTAG, shown in (a) and (b), respectively. Then it integrates these temporal semantic features with the dynamic graph structure features by temporal GNN, as shown in (c).
Node-centric implicit reasoning. As illustrated in Figure 2(a), for node in G, first extract all its interactions with its neighbors, denoted as N₁. Then we sort them by the timestamp and arrange them into sequence, which ensures interactions earlier in time positioned earlier in the sequence. Lastly, we input N₁ into an LLM at once to extract hidden features of N As LLMs are designed for causal modeling of text sequences and share the unidirectional nature with the time sequence of No. this consistency prevents an interaction from observing future interaction text, thereby avoiding leakage of future information. Benefiting from this, single forward pass, all interactions in N₁ can simultaneously acquire recent semantic features from the historical interactions thus enabling efficient reasoning.
Sliding window mechanism. This module aims to solve three challenges: (i) the context window length limitation of LLMs, (ii) the heavy computational resource consumption, (iii) the tendency for LLM performance to degrade with increased input length (Wang et al., 2024a; Levy et al., 2024). Specifically, we partition No into multiple overlapping batches:
B, = {Ial + + * +itc.eeNN.
c
where B, is the i-th batch and is the window length. each batch, the first 9 interactions serve to provide temporal context for the subsequent interactions. Subsequently, for each batch, we organize the interactions chronologically into an input sequence using dataset-specific prompt template (see an example in Figure 6) and feed it into the LLM. Finally, we apply mean pooling to the output hidden layer features for each interaction. The obtained temporal semantic feature for an interaction i is denoted as F E IRduM, where dLMM denotes the dimensionality of the hidden features.
4.2 EXPLICIT REASONING FOR GLOOBAL TEMPORAL SEMANTICS
Complementary to recent temporal features, the occurrence real-world phenomenon often associated with long-range global temporal features. Therefore, this module aims uncover these global temporal semantic features with the explicit reasoning ability of L.Mss, i.e., we directly let [.LMs generate textual information after comprehending DyTAGs.
Specifically, as shown in Figure 2(b), we first evenly partition the interaction sequence N,, into segments by timestamps. Each segment is denoted by S;(1 < s). The partition timestamps denoted by Teb (Fo, (,, f2,.., (.), where to -1 denotes the special starting time. S, consists
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of the chronologically ordered sequence of all interactions occurring between f (exclusive) and i (inclusive). Our motivation is twofold: (i) similar to implicit reasoning, NN, cannot be fed into LLM at once; (ii) performing only a single inference fails to capture the temporal evolution of global semantic features.
Next, we employ an RNN-like reasoning chain structure to empower the LLM with memory capabili- ties. Specifically, let (1 S i S s) represent the textual description of the node's s dynamic features corresponding to the period of Sü, and Dy be the node's 's original textual attribute. The reasoning process can be described as:
(2) where we use dataset-specific template (see Figure for an example) to prompt LLM to uncover the potential global semantics. The reasoning chain structure is designed for the propagation of long-range dynamic features and providing essential background description of the node for the L.M to infer the node's s current features.
D. = L.M (:).
Crucially, this reasoning process designed be cumulative and iterative. The input D;-1 serves as cumulative summary of the node's s entire interaction history up to the start of the current segment S, rather than local feature of the previous segment alone. Consequently, the LLM is tasked with updating this historical summary by integrating the new interactions contained in S. For instance, D1 summarizes S1. while D2 builds upon D1 to incorporate S2, thereby summarizing the combined history. Through this mechanism, the generated description D; encapsulates the semantics from the node's ' history spanning from Si through S, effectively capturing its long-term, global temporal evolution.
In the above process, each interaction is fed into the LLM twice. Consequently, the overall complexity limited O(EE)), highlighting the efficiency of the explicit reasoning process.
4.3 INTEGRATING SEMANTICS AND GRAPH STRUCTURE
Next we present the component to integrate temporal semantics and dynamic graph structure features. For node v and prediction time t, this module aims to obtain an integrated representation, which will be used for downstream tasks. As shown in Figure 2(c), we first divide the model into three components: Recent Semantic (RS) layer, Global Semantic (GS) layer, and graph structure layer. Let L denote the total number of layers and denote the current layer index.
Let V = {Litt, < t, E No.. In Section 4.1, we have obtained the temporal feature F for interaction 1; E N and its corresponding timestamp &,. Following (Xu et al., 2020), we use learnable time encoder to encode the time interval At; t f; into a dd-dimensional vector capture periodic temporal patterns:
Recent Semantic Layer.
R
(FT) g( - +4],
where P(-): Rddu -> Rdd is projector, HII-] denotes concatenation, and g() is the time encoder.
Note that d, & duim and thus the projection can also accelerate computing. Lastly, (R. (0)) are used as the input feature for the RS layer to compute the recent temporal semantics:
R
R R
( Wil
RS Layer ([R))
[RR-") R (-11)
(1-
R
(1-1) W!
(4)
Given the strengths of Transformers in processing sequential data, we employ a Transformer Encoder realize RS_ Rs_Layer(-).
Global Semantic Layer. Similarly, we have obtained the global textual description D; for node e in timestamp in Section 4.2. We use word embedding model (e.g.. BERT) to transform each textual description D, into hidden representation Feb RedeR as global semantic features. To capture the dynamic changes in node features leading up we then calculate the input features G as follows:
G = P' (P
T( (ta)
(5)
where PP(+): ded + Rd is another projector. Lastly, the global feature are updated as:
G (1)
G ( .... G = GS_Layer (GG (1 .... G -(-"1))
(6)

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Published as a conference paper at ICLR 2026
where i max t) ensures that only features up to are considered to prevent future information leakage. Similar to the RS layer, we use a Transformer Encoder to realize GS Layer(-) in our implementation.
Graph Structure Layer. this component, we first explicitly model dynamic graph structures by learning the structural representation ( The graph structure layer is represented as:
( Merge TONN
(MU-)),MPo(C))
(7)
where ( 1) denotes the process of gathering and aggregating features from the neighbors of node v in graph 9 up to time t, M(1-1) is the integrated node feature, and Merge TONN merges and updates M(--1) with MP f). Benefiting from the modularity design DyGRASP, our method is compatible with any existing cessage-passinng temporal GNN. In our implementation, two representative temporal GNNs, DyGFormer (Yu et al., 2023) and TGAT (Xu et al., 2020), are used to demonstrate the compatibility of DyGRASP.
The integrated node feature M aims to fuse features from three components, i.e., recent semantics (R(") 3. global semantics (G. }), and graph structure ( using Merge Layerr))
(G)))
M ( Merge_Layer (R"( (T
(8)
where M dRd represents the node's integrated feature, M is the node raw feature, (*) and (-) are readout functions responsible for summarizing the features from the RS and GS Layer, respectively. We Merge Layer an MLP and (-)-) as a mean pooling operation:
R" ("R(R (R) Mean Peoling (R R .......
(9)
For (.), we simply take the last feature in the sequence, i.e..
Rd R² G
(10)
which is based on following considerations: (i) the Transformer Encoder in the GS Layer(-) inher- ently aggregates information via the attention mechanism, potentially concentrating historical global
semantics in the final feature G (4), (ii) the preceding global reasoning chain employs a RNN-like mechanism to propagate historical global semantic to the final feature. The final output of the model, (LL), serves as the node's s comprehensive feature representation for downstream tasks.
Training for DyGRASP. Following DTGB, We adopt end-to-end training approach and supervise the model training using a binary cross-entropy loss for both the destination node retrieval and future link prediction tasks. Specifically, M! to predict the probability score of future interaction between nodes and v. For each true interaction Ju,opa. occurring at time T, we randomly sample a negative node Uneg to create false interaction Lu,eney which did not actually occur. Finally, we use all the true and false samples for training.
an MLP layer to take the final features (i.e., M!!") and
EXPERIMENTS
In this section, first show the superiority of DyGRASP existing methods across various tasks and validate the generalizability of DyGRASP on multiple LLMs and base temporal GNNs. Then we separately validate the effectiveness of Recent/Global Semantic Reasoning and study the impact of the hyperparameter, as well as investigate the inference efficiency.
5.1 EXPERIMENTAL SETTINGS
Evaluation Tasks and Datasets. Following (Zhang et al., 2024), we evaluate models using the destination node retrieval task with Hit@ 10 metric under transductive and inductive settings and the future link prediction task with AP and ROC-AUC metries. Except for main results, all other experiments destination node retrieval as the evaluation task. We select four datasets from the
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Table 1: Hit 10 (%) results for destination node retrieval. OOM means out of memory. We use boldface and underlining to denote the best and the second-bes, performance, respectively.
Transductive
Inductive
Methods
GDELT
Earon
Googlemap
Stack elec
GDELT
Enron
Googlemap
Stack elec
JODIE
89. 88
91 200
0OM
OOM
68 68.8421.09
78. 78.78e1 65
OOM
OOM
DyRep
65.34
85.79a231
0OM
0OM
61 1.633401
68.53a1
OOM
0OM
CAWN
89. 24
91. 96an87
52,07a087
91.37a
63 34..9
76.48sl es
44. 44.04an74
46,30s2.32
TCL
40.58
87 96
48. 70
84. 14.1.410
69. 20.282t.12
64.9929.9
00.000
37.82+11.58
GraphMixer
88.67 38.67anos
87.36.1
48.34a0.3
93.25am.04
63.92a0.12
66.83a1 08
40.71am119
52,04a0.34
Llama-3.1
41.01
92.09
53.97
34.24
31.75
79.55
43,56
33.59
TGAT
89.97
89.12-021
63.53.0.09
93.74.0.11
65.76-0.46
70.93+085
56.61
54.29:0.47
DyGRASPORAT)
TV
96.25a1.11
82.79
99.74x0.44
71,78:0:38
86.76+207
78.63+0
99.77.a.9
Improv. 1
+1.99
+7.13
+19.26
+6.00
+6.02
+15.83
+22.02
+45.48
DyGFormer
6444222
92.04=1.7
51,3221.15
94. 19
72.49e0:37
82.52
13.842192
56,23-0.52
93.24
99.40hm.29
85.88-264
99.59xa22
75.93mn.16
95. 18
81.14:2.12
99.74-0.14
Improv.
+1.60
+,33
+34.56
+5,20
+3.44
+2.66
+37.00
+43.31
DTGB benchmark (Zhang et al., 2024): GDELT, Enron, Googlemap, and Stack clec, which cover diverse graph scales and domains.
Model Configurations and Baselines. We choose seven state-of-the-arr temporal GNNs as baselines: JODIE (Kumar al., 2019), DyRep (Trivedi et al., 2019), CAWN (Wang al., 2021c), TCL (Wang et al., 2021a), GraphMixer (Cong et al., 2023), TGAT (Xu et al., 2020), and DyGFormer (Yu et al., 2023). We also evaluate the performance of Llama-3.1 --s-truucttt (Grattafiori al., 2024), the primary LLM used in DyGRASP, as another baseline. For DyGRASP, we primarily utilize DyGFormer as the base temporal GNN and include TGAT to validate the generalizability. We mainly employ 1lama-3.1-8D / Llama-3,1 1-8.1-1---tttuue for implicit and explicit reasoning, respectively. More details can be found in Appendix E.
5.2 MAIN RESULTS
Table 2: Results for future link prediction. OOM means out of memory. We use boldface and underlining to denote the best and the second- best performance, respectively.
Metric
Methods
GDELT
Enron
Transductive
Gonglemap
Stack elec
GDELT
Enron
Inductive
Googlemap
Stack elec
JODIE
05.5620.05
96.70a134
0OM
0OM
87.94
26
OOM
00M
DyRep
94.55.019
94 99
00M
00M
85.02=1 59
85 84
00M
00M
CAWN
96.57.009
97.85
82.78.0.0
95.551006
88.65
92.91.03
78.67.063
80.4810.11
TCL.
96.63,0.14
96.31a1.4
80.71
89.5829.50
89.95.838
27.4.7
76.13
73.09:10.44
AP
GraphMixer
95,95a005
96.01a022
80.60sn.0
96. 17s000
87.30an
87 60.600
76.13
82,75
TGAT
96.41.002
96.78%n04
87.71,0u7
96. 59.09n
88.30.0
59.544
84.97a033
84.2410.51
DyGFormer
97.02a0.33
97. 97.86mm.
$1.63a004
70snoo
91,09an.15
93 60-0
77.236002
84.6720.34
Ilama-3.1
82.23
67.80
60.35
73.61
64.08
58.93
DyGRASPrICATI
97.78.1.4
98 mn
96.23.1.6
98 25
93.05:4.43
91 166666333
95.31
88. 11:6.11
DyGASPPiyGeeme!
97.40.001
94.85.022
98.42sa.22
92.23an02
93.68.5.20
88.501a9.00
JODIE
96.26x007
97. 27.0
00M
0OM
88 18.03-amm
90.21-4552
0OM
00M
DyRep
95,23a0.11
95.47au22
0OM
0OM
85,49an
0OM
0OM
CAWN
96.694905
97 80-00117
83 53
96 38
88. 29
91 93
78. 9..227677
80 20.4440.22
TCI.
96.77.0.11
96.54-1.94
80.90:a41
89.23.11.54
89.56
08.08-4.1
75.45
73.16
AUC
GraphMiser
96.14u0.02
96.
80.84.007
96.8 86.86.0002
87.19
36
75.2
83. 25..55
TGAT
96.58s0002
96. 26.92am04
88, 192033
97. 97.1120000
88.33an.12
89.4628134
85. 3-13300
84.566.0229
DyGFormer
10.10
81 $1.91
97.31
90.59
68
76.34
85. 35.24
Llama-3.1
83.31
86,86
69.38
55.41
74.03
78.26
66.17
56.04
DyGRASPrrenT
97,2240
34
94. 87-004
98. 7940.0
900 to
37.38-333
93.424000
85, 85.91
DyGRASPonyElveee)
97.60..0.4
98.86
94,899..36
98.874m.0
92,19uuu
94,85
93,588.615
86.6918.20
DyGRASP consistently outperforms all existing methods with substantial improvements. As shown in Table 1 we can observe that under both transductive and inductive settings, Dy- GRASP(DyGFomeer) outperforms existing methods on all datasets, including traditional temporal GNNs and 1--ma--.--BB-Instiie
demonstrating the superiority of DyGRASP. Notably, benefiting from the strong generalization of LLMs on semantic understanding, DyGRASP achieves higher improvements in the inductive setting compared to the transductive setting. Moreover, shown in Table 1 and Figure 8, the performance improvement over the base temporal GNN is more pronounced on datasets with richer textual information, highlighting the important role of the LLM in our method.
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Published as a conference paper at ICLR 2026
Traakadir
Taladas
ALM
Skte
9331
31.73
61.33
$3.44
82
12.42
Eareo
Googlemnggcc CT
Enroa
Gorglemap_CC
Figure 3: Results with different LLM backbones. Our method is compatible with various LLMs.
Figure 4: Results of ablation studies. Both global and recent modules contribute to our method.
DyGRASP demonstrates generalizability across both base temporal GNNs and LLMs. As shown in Table the results indicate that DyGRASP consistently yields significant performance improvements compared to the original temporal GNNs when either using TGAT or DyGFormer as the base temporal GNN. Furthermore, we validate the effectiveness of DyGRASP with three different families of LLMs, including Qwen2.5, Mistral, and Llama-3.1, on the Enron and Googlemap datasets. As shown in Figure 3, DyGRASP consistently outperforms the base temporal GNN regardless of the specific L.M and exhibits minimal variation across different LLMs, demonstrating the exceptional general applicability and compatibility of DyGRASP.
5.3 ABLATION STUDIES AND ANALYSES
Ablation studies. To validate the components of our method, we evaluate the following ablations: (i) removing the global semantic layer (denoted -Global); (ii) removing the recent semantic layer (denoted as -Recent); (iii) removing both layers (denoted as Recent&&GGlobal)
As illustrated Figure under both the transductive and inductive settings, adding either the recent or global temporal semantic reasoning module individually leads to improvements in performance. Furthermore, the performance obtained with both modules added simultaneously surpassed those achieved when only a single module is incorporated, which strongly confirms the complementarity of gecent-global features and the effectiveness of both LLM reasoning designs.
Hyperparameter Sensitivity Analysis. Next, we investigate the key hyperparameters of DyGRASP, including the number global segments s in global semantic reasoning and the maximum interaction length e in recent temporal semantic reasoning. We separately study the effects of and using the features given by cither the recent semantic layer or the global semantic layer. We report results on the Googlemap dataset, while other datasets show similar trends.
Number of global segments s. As shown in Figure 5, model performance consistently improves as increases. We hypothesize that it is because larger number of segments leads to more frequent reasoning for the LLM and smaller intervals between two reasoning time points, which allows the I.LM to give smoother global temporal semantic variations. However, higher number of also leads to proportional increase in the inference time.
Maximum interaction length As il- lustrated in Figure 5, model perfor-
mance initially improves significantly as e increases but then decreases. A plausible reason that, compared to independent encoding of text attribute (c = 0), the recent semantic reasoning captures temporal semantic dependen- cies between interactions. larger c allows observation of more historical interactions, enriching the recent tem- poral semantics thus leading to perfor- mance gains. However, when e sur- passes 16, performance starts decline plausibly because of increased input length, i.e., the perfor-
Transtncthro Indactive
55
Transdothiv
Inductive
H1t0 9g)
50
11N@8:0 10 (96)
50
45
85
40
40-
35
Value of
Value of
Figure 5: Results of sensitivity analysis.

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Published as a conference paper at ICLR 2026
mance of LLMs can degrade significantly input length increases (Wang et al., 2024a; Levy et al., 2024).
Efficiency Analysis. Due to the decoupling of LLM inference from the online prediction task, the inference of actual prediction task can be as fast as a lightweight temporal GNN. Furthermore, the computationally intensive LLM reasoning is offline, one-time pre- rreprrcessiig step extract temporal semantic features. These features are then stored (e.g.. in vector database) for fast retrieval. Here we first investigate the inference efficiency of LLMs in our method and then provide speed comparison on online prediction task. The results are shown in Table and Table 4 where the inference time of LLMs is measured by Nvidia-A 100 GPU-Days.
Table 3: Speed comparison (edges/second) on online prediction task.
Dataset
JODIE
DyRep
CAWN
TCL.
GraphMixer
TGAT
DyGFormer
Llama-3.1
DyGRASP (Ours)
Enron
3,372
2,872
313
1,157
2,410
587
1.077
729
Googlemap CT
0OM
0OM
307
2,621
3,098
545
1,165
13
757
Online prediction efficiency. Despite incorporating LLM, DyGRASP's inference speed is com- petitive with other TGNNs and is notably faster than complex attention based (e.g.. TGAT) and Handom-walk-based (e.g., CAWN) models. This speed is orders of magnitude faster than that of a pure LLM baseline (e.g., Llama 3.1), demonstrating the high efficiency of our method at online prediction.
Table Token consumption and inference time of different components.
Reasoning configuration
Token Consumption Googlemap
Inference time Googlemap
GDELT
Enron
Stack_elee _lee
GDELT
Enron
Stack_ele
Recent wio node-centric reasoning
1,68B
3.62B
2,428
7,00B
2.37
4.34
2.45
7.21
node-ventric reasoning
0.35B
0,96B
0.16
0.84
1.21
Global Reasoning
0.03B
0.25B
0,52R
0.48B
0.04
0.37
0.89
1.42
Recent semantic reasoning efficiency As discussed in Section 4.1, introduce node-centric implicit reasoning to address the inference efficiency. The results show that the node-centric reasoning method significantly reduces the reasoning time across all datasets, which is consistent with our theoretical analysis. This efficiency gain stems from a critical trade-off off design: by shifting from an edge-centric to a node-centric view, we reduce the theoretical complexity from prohibitive O(EE) x d) manageable O Furthermore, the sliding window size e acts control lever for this trade-off. We select e to maximize the capture historical context while avoiding the computational burden and performance degradation associated with excessively long LLM inputs.
Global semantic reasoning efficiency. We also report the inference time in Table 4. We could observe that the time consumption for this module is also low, highlighting the efficiency of DyGRASP. This performance relies on the granularity trade-off governed by the number of segments 8. Instead of processing node's entire vast history once, partition it into segments. While larger provides more fine-graine fine-grained snapshots semantic evolution, it leads to a linear increase inference time. Our design balances this trade-off to ensure computational feasibility while effectively capturing long-term global dynamics.
CONCLUSION
In this paper, we propose DyGRASP, an efficient and effective model for DyTAGs by capturing the recent-global temporal semantics. We leverage the implicit and explicit reasoning capabilities of LLMs to uncover the recent and global semantics DyTAGs, respectively, and integrate these tempo- ral semantic features with dynamic graph structural features through temporal GNN. Experiments show that DyGRASP achieves significant improvement of up to 34% the Hit@ metric for the destination node retrieval task compared ofate-of-the-art methods.
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7 ACKNOWLEDGMENT
This work was supported in part by the National Natural Science Foundation of China (No. 92570113, 62472018).
REFERENCES
Marah Abdin, Jyoti Aneja, Harkirat Behl, Sébastien Bubeck, Ronen Eldan, Suriya Gunasekar, Michael Harrison, Russell Hewett. Mojan Javaheripi, Piero Kauffmann, et al. Phi-4 technical report. arXiv preprint arXiv:2412.088000 2024.
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et Gpt-4 technical report. arXiv preprint arXiv:2303.08774 2023.
Ebtesam Almazrouei, Hamza Alobeidli, Abdulaziz Alshamsi, Alessandro Cappelli, Ruxandra Cojo- caru, Merouane Debbah, Etienne Goffinet, Daniel Heslow, Julien Launay, Quentin Malartic, et al. Falcon-40b: an open large language model with state-of-the- art performance, 2023.
Azadeh Beiranvand and Seyed Mehdi Vahidipour. Integrating structural and semantic signals in text-attributed graphs with bigtex. arXiv preprint arXiv:2504.12474 2025.
Haibo Chen, Xin Wang, Zeyang Zhang, Haoyang Li, Weigao Wen, Ling Feng, and Wenwu Zhu. Curriculum gnn-llm alignment for text-attributed graphs 2024.
Eli Chien, Wei- Wei-Chen Cheng Chang, Cho-Jui Hsieh, Hsiang- Fu Yu, Jiong Zhang, Olgica Milenkovic, and Inderjit S Dhillon. Node feature extraction by self supervupe multi-scale neighborhood prediction. In International Conference on Learning Representations.
Weilin Cong, Si Zhang, Jian Kang, Baichuan Yuan, Hao Wu, Xin Zhou, Hanghang Tong, and Mehrdad Mahdavi. Do really need complicated model architectures for temporal networks? In 1tth International Conference on Learning Representations, ICLR 2023, 2023.
Songgaojun Deng, Huzefa Rangwala, and Yue Ning. Learning dynamic context graphs for predicting social events. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp. 1007-1016, 2019.
Jacob Devlin, Ming- Ming-Wei Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 conference of the North American chapter of the association for compulational linguistics: human language technologies, volume (long and short papers), pp. 4171-4186, 2019.
Keyu Duan, Qian Liu, Tat-Seng Chua, Shuicheng Yan, Wei Tsang Ooi, Qizhe Xie, and Junxian He. Simteg: A frustratingly simple approach improves textual graph learning. arXiv preprint arXiv:2308.022 02565, 2023.
C Lee Giles, Kurt D Bollacker, and Steve Lawrence. Citeseer: An automatic citation indexing system. In Proceedings of the third ACM conference on Digital libraries, pp. 89-98, 1998.
Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, et al. The llama 3 herd of models. arxiv preprint arXh::2407.2178. 2024.
Alessio Gravina, Giulio Lovisotto, Claudio Gallicchio, Davide Bacciu, and Claas Grohnfeldt. Long range propagation on continuous-time dynamic graphs In Proceedings of the 41st International Conference on Machine Learning, pp. 16206-16225, 2024.
Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al. Deepseck-rl: Incentivizing reasoning capability in Ilms via reinforcement learning. arXiv preprint arXiv:2501. 1204. 2025.
11

---

Published as a conference paper at ICLR 2026
Zirui Guo, Lianghao Xia, Yanhua Yu, Yuling Wang, Zixuan Yang, Wei Wei, Liang Pang, Tat-Seng Chua, and Chao Huang. Graphedit: Large language models for graph structure learning. arXiv preprint 4rX1v:2402.15183, 2024.
Will Hamilton, Zhitao Ying, and Jure Leskovec. Inductive representation learning on large graphs. Advances in neural information processing systems, 30, 2017.
Xiaoxin He, Xavier Bresson, Thomas Laurent, Bryan Hooi, et al. Explanations as features: Llm-based features for text-attributed graphs. arXiv preprint arXiv:2305. /9523, 2(4):8, 2023.
Xiaoxin He, Xavier Bresson, Thomas Laurent, Adam Perold, Yann LeCun, and Bryan Hooi. Harness- ing explanations: Llm-to-Im interpreter for enhanced text-attributed graph representation learning. In 12th International Conference on Learning Representations, ICI. 2024, 2024.
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. Open graph benchmark: Datasets for machine learning on graphs. Advances in neural information processing systems, 33:221 18-22133, 2020.
Yifan Huang, Clayton Thomas Barham, Eric Page, and PK Douglas. Thrgm:: Social theory-driven network simulation. In NeurlPS 2022 Temporal Graph Learning Workshop, 2022.
Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de Las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Amn Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed. Mistral 7b. CoRR, abs/2310.06825. 2023. doi: 10. 08550/ARRIV.2310.0668 URL https: doi. org/10. 48544 arxiv. 2310. 0 06855.
Shima Khoshraftar, Niaz Abedini, and Amir Hajian. Graphit: Efficient node classification on text-attributed graphs with prompt optimized lims. arXiv preprint arXiv:2502..1522 2025.
Alexy Khrabrov and George Cybenko. Discovering influence in communication networks using dynamic graph analysis. In 2010 IEEE Second International Conference on Social Computing, pp. 288 294. IEEE, 2010.
Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. International Conference on Learning Representations, 2014.
Thomas N Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. In International Conference on Learning Representations, 2017.
Srijan Kumar, Xikun Zhang, and Jure Leskovec. Predicting dynamic embedding trajectory in temporal interaction networks. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp. 1269-1278, 2019.
Runlin Lei, Jiarui Ji, Haipeng Ding, Lu Yi, Zhewei Wei, Yongchao Liu, and Chuntao Hong. Exploring the potential of large language models predictors in dynamic text-attributed graphs. arXiv preprint arXiv:2503 03258, 2025.
Mosh Levy, Alon Jacoby, and Yoav Goldberg. Same task, more tokens: the impact of input length on the reasoning performance of large language models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 15339-153533 2024.
Guohao Li. Matthias Müller, Bernard Ghanem, and Vladlen Koltun. Training graph neural networks with 1000 layers. In International conference on machine learning, pp. 6437-6449, 2021.
Hao Li, Hao Wan, Yuzhou Chen, Dongsheng Ye, Yulia Gel, and Hao Jiang. Tmetanet: Topological meta-learning framework for dynamic link prediction. In Forty-secona International Conference Machine Learning, 2025.
Hanmo Liu, Shimin Di, Xun Jian, Yue Wang, Lei Chen, et al. A selective learning method for temporal graph continual learning. In Forty-second International Conference Machine Learning, 2025a.
12

---

Published as a conference paper at ICLR 2026
Hanwen Liu, Longjiao Zhang, Rui Wang, Tongya Zheng, Sai Wu, Chang Yao. and Mingli Song. Salom: Structure aware temporal graph networks with long-shor memory updater. In The Thirty- ninth Annual Conference on Neural Information Processing Systems, 2025b.
Hao Liu, Jiarui Feng, Lecheng Kong, Ningyue Liang, Dacheng Tao, Yixin Chen, and Muhan Zhang. One for all: Towards training one graph model for all classification tasks. In The Twelfth International Conference on Learning Representations, 2024.
Xiao Luo, Jingyang Yuan, Zijie Huang, Huiyu Jiang, Yifang Qin, Wei Ju, Ming Zhang, and Yizhou Sun. Hope: High-order graph ode for modeling interacting dynamics. In International conference on machine learming. pp. 23124-23133 23139, 2023.
Yuhong Luo and Pan Li. Neighborhood-awann scalable temporal network representation learning, In Learning on Graphs Conference, pp. 1-122022
Yao Ma, Ziyi Guo, Zhaocun Ren, Jiliang Tang, and Dawei Yin. Streaming graph neural networks. In Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, pp. 719-728, 2020.
Péter Mernyei and Cätälina Cangea. Wiki-es: A wikipedia-based benchmark for graph neural networks. arXiv preprint arXiv:2007.02901, 2020.
Farimah Poursafaei, Shenyang Huang, Kellin Pelrine, and Reihaneh Rabbany. Towards better evaluation for dynamic link prediction. Advances in Neural Information Processing Systems, 35: 32928-32941, 2022.
Aniq Ur Rahman and Justin Coon. Node feature forecasting in temporal graphs: an interpretable online algorithm. Transactions on Machine Learning Research, 2025.
Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca, Davide Eynard, Federico Monti, and Michael Bronstein. Temporal graph networks for deep learning on dynamic graphs. ICML 2020 Workshop Graph Representation Learning, 2020.
Amit Roy, Ning Yan, and Masood Mortazavi. Llm-driven knowledge distillation for dynamic text-attributed graphs. arXiv preprint arXiv:2502. 10914, 2025.
Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad. Collective classification network data. Al magazine, 29(3):93-92 2008
Weiping Song, Zhiping Xiao, Yifan Wang, Laurent Charlin, Ming Zhang, and Jian Tang. Session- based social recommendation via dynamic graph attention networks. In Proceedings of the Twelfth ACM international conference on web search and data mining, pp. 555-563, 2019.
Haoran Tang, Shiqing Wu, Guandong Xu, and Qing Li. Dynamic graph evolution learning for recommendation. In Proceedings of the 46th international acm sigir conference on research and development in information retrieval. pp. 1189-1598, 2023.
Jiabin Tang, Yuhao Yang. Wei Wei, Lei Shi, Lixin Su, Suqi Cheng, Dawei Yin, and Chao Huang. Graphgpt: Graph instruction tuning for large language models. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 491-500, 2024.
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arX.v:2307.09288, 2023.
Rakshit Trivedi, Mehrdad Farajtabar, Prasenjeet Biswal, and Hongyuan Zha. Dyrep: Learning representations over dynamic graphs. In International conference on learning representations, 2019.
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. Graph attention networks. In International Conference on Learning Representations, 2018.
13

---

Published as a conference paper at ICLR 2026
Lu Wang, Xiaofu Chang, Shuang Li, Yunfei Chu, Hui Li, Wei Zhang, Xiaofeng He, Le Song. Jingren Zhou, and Hongxia Yang. Tel: Transformer based dynamic graph modelling via contrastive learning. arXiv preprint arXtv:2105.07444 2021a.
Xindi Wang, Mahsa Salmani, Parsa Omidi, Xiangyu Ren, Mehdi Rezagholizadeh, and Armaghan Eshaghi. Beyond the limits: survey of techniques extend the context length in large language models. In Proceedings of the Thirty-Thine International Joint Conference on Artificial Intelligence, pp. 8299-8307, 2024a.
Xuhong Wang. Ding Lyu, Mengjian Li, Yang Xia, Qi Yang, Xinwen Wang, Xinguang Wang, Ping Cui, Yupu Yang, Bowen Sun, et al. Apan: Asynchronous propagation attention network for real-time temporal graph embedding. In Proceedings of the 2021 international conference on management of data, pp. 2628 2638, 2021b.
Yanbang Wang. Yen- Yu Chang, Yunyu Liu, Jure Leskovec, and Pan Li. Inductive representation learning in temporal networks via causal anonymous walks. In International Conference Learning Representations (ICLR), 2021c.
Yaoke Wang, Yun Zhu, Wenqiao Zhang, Yueting Zhuang, Liyunfei Liyunfei, and Siliang Tang. Bridging local details and global context in text-attributed graphs. In Proceedings of the 2024 Conference Empirical Methods in Natural Language Processing, pp. 14830-14841, 2024b.
Wei Wei, Xubin Ren, Jiabin Tang, Qinyong Wang, Lixin Su, Suqi Cheng, Junfeng Wang. Dawei Yin, and Chao Huang. Limrec: Large language models with graph augmentation for recommendation. In Proceedings of the 17th ACM International Conference on Web Search and Data Mining, pp.
806-815, 2024.
Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger. Sim- plifying graph convolutional networks. In International conference machine learning, pp. 6861-6871, 2019.
Yuxia Wu, Shujie Li, Yuan Fang, and Chuan Shi. Exploring the potential of large language models for heterophilic graphs. arXiv preprint arXiv:2408.14135 14134, 2024.
Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, and Kannan Achan. Inductive representa- tion learning on temporal graphs. International Conference on Learning Representations (ICLR), 2020.
Yuanyuan Xu, Wenjie Zhang, Xuemin Lin, and Ying Zhang. Unidyg: A unified and effective representation learning approach for large dynamic graphs IEEE Transactions on Knowledge and Data Engineering. 2025.
Rui Xue, Xipeng Shen, Ruozhou Yu, and Xiaorui Liu. Efficient large language models fine-tuning on graphs. arXiv preprint arXiv:2312.04737, 2023.
Hao Yan, Chaozhuo Li, Ruosong Long, Chao Yan, Jianan Zhao, Wenwen Zhuang, Jun Yin, Peiyan Zhang, Weihao Han, Hao Sun, al. A comprehensive study text-attributed graphs: Bench- marking and rethinking. Advances in Neural Information Processing Systems, 36: 16:17228-1726,, 2023.
An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, et al. Qwen2. 5 technical report. arXiv preprini 15Xiv:2412.15115, 2024.
Junhan Yang. Zheng Liu, Shitao Xiao, Chaozhuo Li, Defu Lian, Sanjay Agrawal, Amit Singh, Guangzhong Sun, and Xing Xie. Graphformers: Gnn-nester transformers for representation learning textual graph. Advances in Neural Information Processing Systems, 34:28798-28811 2021.
Le Yu, Leilei Sun, Bowen Du, and Weifeng Lv. Towards better dynamic graph learning: New architecture and unified library. Advances in Neural Information Processing Systems, 36:67686 67700, 2023.
14

---

Published as a conference paper at ICLR 2026
Jiasheng Zhang, Jialin Chen, Menglin Yang, Aosong Feng, Shuang Liang, Jie Shao, and Rex Ying. Digb: A comprehensive benchmark for dynamic text-attributed graphs. In The Thirty-eight Conference Neural Information Processing Systems Datasets and Benchmarks Track, 2024.
Mengqi Zhang, Shu Wu, Xueli Yu, Qiang Liu, and Liang Wang. Dynamic graph neural networks for sequential recommendation. IEEE Transactions Knowledge and Data Engineering. 35(5): 4741-4753, 2022.
Siwei Zhang, Yun Xiong, Yateng Tang, Xi Chen, Zian Jia, Zehao Gu, Jiarong Xu, and Jiawei Zhang. Unifying text semantics and graph structures for temporal text-attributed graphs with large language models. arXiv preprint arXiv:2503: 14411, 2025.
Yun Zhu, Yaoke Wang, Haizhou Shi, and Siliang Tang. Efficient tuning and inference for large lan- guage models on textual graphs. In Proceedings of the Thirty-Thing International Joint Conference on Artificial Intelligence, pp. 5744-5742, 2024.
Yun Zhu, Haizhou Shi, Xiaotang Wang, Yongchao Liu, Yaoke Wang, Boci Peng, Chuntao Hong, and Siliang Tang. Graphelip: Enhancing transferability in graph foundation models for text-attributed graphs. In Proceedings of the ACM on Web Conference 2025, pp. 2183-2197, 2025.
A LIMITATIONS
One limitation of our paper that we only test our method on DTGB benchmarks, considering that it is the only publicly available benchmark for DyTAGs. Experiments more diverse real-world datasets and tasks will further demonstrate the usefulness of DyGRASP. Another direction worthy of exploration is extending our method into more complicated graph types, such DyTAGs with heterogeneous nodes and edges or hyper-relations.
B NOTATIONS
In this section, we provide important notations used in this paper, detailed in Table 5.
Table 5: Notations and descriptions.
Notation Description
A dynamic text-attributed graph. edge set DyTAG. timestamp set of TAG. The node text attribute set of the DyTAG.
D
R
The edge text attribute set of the DyTAG.
N
The chronological list of interactions with node v's first-hup neighbors. The state of the DyTAG before timestamp t. An interaction between two nodes.
G
fu.e
F
The raw recent temporal semantic feature of an interaction I, segment of interactions, which contains interactions occurring between two partition timestamps.
The of all partition timestamps.
description node before vertain timestamp S,,
Feb
The raw global tomporal semantic feature of node before the 2th partition timestamp.
R
The processed recent temporal semantic feature of the 1-th layer. processed global temporal semantic feature the [-th layer.
G
Sio
The graph structure feature given by temporal GNN the I-th layer
R((-)
readout function get the recent semantic feature certain node from list R())
R*(-)
A readout function to get the global semantic feature of a certain node from list of
C PROOF OF REASONING COMPLEXITY
In this section, we give a complexity proof of recent semantic reasoning in Section 4.1 (i.e., the number of consumed input tokens), including both intuitive approach without node-centric reasoning
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and the proposed method with node-centric reasoning. For simplicity of analysis, assumee that the degree of each node is and the interaction of each node with its d neighbor nodes occurs at distinct times t, i I d), where t, =, if and only if i j. In this case, we have O((ET) O(V) x d).
Complexity of intuitive approach without node-centric reasoning. Consider node u and its historical interaction sequence:
Nu = ( Ty 1H), where t S i2 S ... S ta.
(11)
For historical interaction I,", assume its interacted node is u. To provide temporal context for 1," the input content to the LLM is:
Input (7)1k E م
(12)
which totals 2 x (i (i-11) 1) + interactions. Therefore, the input complexity for a single node is:
o 2 - 1))=
(13)
Since each edge belongs to two nodes, the total input complexity for all nodes is:
0 M 2 O(dP) O( d)-
(14)
Complexity of our proposed method with node-centric reasoning. Consider node u and its historical interaction sequence No., as shown in Eq. 11. This sequence is divided by sliding window into [dd - input batches, where is the window length. The complexity each batch is O(c).
Therefore, the input complexity for node is:
O(( d
-) x Odd).
(15)
The final input complexity for all nodes is O(d DJ) = O((E)).
D PROMPT FOR LLM REASONING
In this section, we provide dataset-specific prompt templates used for LLM reasoning, including implicit reasoning for recent temporal semanties and explicit reasoning for global temporal semantics. We select prompt for Googlemap as an example. The remaining dataset follows similar structure.
Below series of reviews business entities given by (user_ description), user of Google map. These reviews arranged chronological order from earliest latest. Capture the potential relationships between them.
Timestamp: (timestamp A)
Business Description: ((business description A)' Review: "review_conter www.eoneeett.. A)'
Timestamp: (timestamp B)
Business Description: (business description BY Review: 'rreiee, content B)'
Timestamp: (timestamp C}
Business Description: "[business description Cy*
content CY Review: (review "reviee_content
Figure 6: The prompt used for Googlemap of recent semantic reasoning.
E DETAILED EXPERIMENTAL SETTINGS
Model Configurations. For DyGRASP, we primarily utilize DyGFormer as the base temporal GNN and include TGAT to validate the generalizability. We mainly employ Llama-3.1-88 Llama- 3. Instructtt for implicit and explicit reasoning, respectively. We also include Qwen2.5-7B / Qwen2.5-7B-Insttuu (Yang et al., 2024) and Mistral-7B / Bestral--B-Instruaa (Jiang et al., 2023) to verify the generalizability of DyGRASP across different LLMs. We set c to 64 for GDELT and 16 for the remaining datasets. is selected based on following considerations:
Data Characteristics: c is adapted to the average node degree. Datasets with denser histories, like GDELT, require larger capture sufficient temporal context.
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Instruction
information about user of Google map description timestamp destrdee time]. Taking into account its recent reviews on business entities, please provide new summative description of this user timestamp (current describe time) based on your understanding. Return the format "Description:<Your Description
###
User Information
(raw_user_dees) juser_description
### Description
cest_user_dessciie
## Recent Reviews
Timestamp: (timestamp A)
Business Description: "business description AJ' Review: [review_content A]'
Timestamp: (timestamp B)
Business Description: ([buiiness description BY| Review: "review_content
content Bj'
Timestamp: (timestamp C)
Business Description: (business description C)
Review: Freview_conteni C)'
Figure 7: The prompt used for Googlemap of global semantic reasoning.
Computational & LLM Constraints: A larger increases input sequence length, posing challenges for computational resources and the LLM's hard token limit. For datasets with lengthy text attributes (e.g.. Enron and Stack_elec), is carefully bounded by these resource constraints.
Model Performance: Crucially, LLM performance degrades on excessively long inputs, es- pecially as they approach or exceed the model's s context window-a phenomenon we analyze Figure This the most critical interaction with token limits. We select e to maximize information gain while avoiding this performance cliff.
We set to 8 across all datasets. The impact of these two hyperparameters investigated in Section 5.3. The total number of layers is set to 2.
Implementation Details. We adopt the experimental setup provided by DTGB benchmark. All datasets split into training, validation, and test sets with ratio of 0.7:0.15:0.1 All models trained using the Adam (Kingma & Ba, 2014) optimizer with batch size of 256 and a learning rate of 0.0001. Training runs for up 50 epochs, with validation performed every epochs. We employ an early stopping mechanism with a patience of 5 epochs. Model-specific hyperparameters for each baseline are set according to the optimal values recommended by the DTGB benchmark (see Table 7 for details). Text attributes within the datasets, as well as the text generated by the LLM during explicit reasoning, are vectorized with bast-base-uncased model (Devlin et al., 2019). All training was conducted on NVIDIA A100 40G GPUs.
E. DESCRIPTIONS OF DATASETS
We use four datasets for the experiments, including GDELT, Enron, Googlemap and Stack Stack_elec, We provide the statistics of datasets in Table We also summarize the text attribute length of these datasets in Figure 8.
Table 6: Statistics of datasets.
Dataset
Nodes
Edges
Domain
Timestamps
Bipartite Graph
GDELT 6,786
1,339,245
Knowledge graph
2,591
X
Enron 42,711
797,907
E-mail R-commerce
1,006
Googlemap
111,168
1,380,623
55,521
Stack_ 397,702
1,262,225
Multi-round dialogue
5,224
We provide descriptions of these datasets follows.
this dataset comes from the "Global Database of Events, Language, and Tone" project, which aims to build a catalog of political behavior covering countries worldwide. this dataset, nodes represent political entities and the node text attribute is the entity name while edges represent political relationships or actions between entities and the edge text attribute represents the description of the behavior.
GDELT:
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Stack_elec
Enron
Googlemap_com
GDELT
200
400
600
800
1000
1200
Node text attribute length
Edge text attribute length
Figure 8: Text attribute length used DyTAG datasets.
Enron: this dataset originates from the email communications of employees at the Enron energy company. Here, nodes represent employees and the node text attribute is the employee's 's email address while edges represent the sending emails and the edge text attribute is the email content.
this dataset is extracted from the Connecticut State (CT) portion of the "Google Local Data" project, containing business review information from Google Maps. In this dataset, nodes users or businesses and the node text attribute is the user name business description while edges represent reviews from users to businesses and the edge text attribute is the specific review content.
Googlemap:
Stack elec; this dataset comes from anonymized data of user question muestion-and-aae and-answar content related to electronics technology on the Stack Exchange website. In this dataset, nodes are questions or users on the site and the node text attribute the question description or user profile while edges represent answers or comments by users on questions and the edge text attribute is the content of the answer or comment.
E.2 DESCRIPTION OF BASELINES
In this section, we provide detailed descriptions of the baselines.
JODIE (Kumar et al., 2019): JODIE is a coupled recurrent neural network model that learns dynamic embedding trajectories for users and items based on their temporal interactions. JODIE utilizes a novel projection operator to predict the future trajectory of embeddings and employs a scalable t-Batch training algorithm, significantly outperforming baseline methods in future interaction and user state change prediction tasks.
DyRep (Trivedi et al., 2019): DyRep is a framework for learning representations on dynamic graphs by modeling topological evolution and node interactions as two distinct but coupled temporal point processes. It uses a temporal-attentive network learn evolving node embeddings that capture the interplay between these structural and activity dynamics over continuous time.
CAWN (Wang et al., 2021c): CAWN is neural network model for inductive representation learning temporal networks. CAWN captures network dynamics using temporal random walks and a novel anonymization strategy, outperforming prior methods in predicting future links, particularly on unseen parts of networks.
TCL (Wang al., 2021a): TCL is Transformer-based dynamic graph modeling method, which optimizes dynamic node representations through contrastive learning. TCL adopts two-stream encoder architecture to respectively process the temporal neighborhood information of the target interaction nodes and fuses them at the semantic level via co-attentional Transformer.
GraphMixer (Cong et al., 2023): GraphMixer is conceptually simple architecture for temporal link prediction using only MLPs and neighbor mean aran--poolin,, intentionally avoiding complex RNN and self-attention mechanisms. Despite its simplicity, Graph achieves tate-of-the-art
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performance benchmarks, demonstrating better results, faster convergence, and improved generalization compared to more complicated models.
TGAT (Xu et al., 2020): TGAT utilizes a self-attention mechanism combined with a novel functional time encoding technique based harmonic analysis. TGAT efficiently aggregates neighborhood information to generate time-aware embeddings for both existing and newly appearing nodes.
DyGFormer (Yu et al., 2023): DyGFormer a new Transformer-base architecture for dynamic graph learning that uses neighbor co-occurrence encoding and sequence patching to effectively capture node correlations and long-term dependencies from historical first-hop interactions.
E.3 CONFIGURATIONS OF DIFFERENT METHODS
We present the hyperparameters for all baselines in Table 7, which are recommended by the official DTGB benchmark.
Table 7: Hyper-parameter setting for all baselines.
Metheds
heads
drepout
time feat_ dim
channel embeedinn dim
patch_
max_inuut_ seputtseqeence_Jaae
10D
100
DyRep
100
CAWN
100
TCL
100
GraphMiiser
0.1
100
TGAT
100
Dy Gommer
0.1
100
50
Methods
depths
A walk haads
walk length
position Eeal dim
neighbors
sample neighbor strategy
time scaling factor
JODIE
10
socent
DyRep CAWN
10
roccul
765
32
dine_jmenn jisa_jjmrvaaaawa
le
TCL
20
recent
GrapaMiser
20
recent
TGAT
20
recent
DyGeereer
recent
E.4 COMPARISON AGAINST EXISTING LLM-FOR-DYTAGE METHOD
Here we provide a comparison with LKD4DyTAG, Note that the source code LKD4DYTAG not available, so we directly compare with their reported results. The results are presented below.
Table 8: Comparison on future link prediction against LKD4DYTAG.
Transductive
Inductive
Metrie
Methods
GDELTT
Googlemap CT
Avg.
GDELT
Googlemap.
Avg.
LKD4DyTAG
97.76
99.43
44
96.21
93.69
95.50
88,90
92.70
AP
DyGRASP (TGAT)
97.78
98.04
96.23
97.35
93.05
91.66
95.31
93.34
DyGRASP (DyGFonmer)
97.40
98.81
97.02
93.68
93.65
LKD4DyIAG
99.98
98.87
91.27
96.71
95.77
93.67
88,57
92.67
AUC
DyGRASP (TGAT)
97.22
98.34
94.87
96.81
90.74
92.38
93.47
92.20
DyGRASP (DyGFormer)
97.60
98.86
94.89
97.12
92.19
94.85
93,58
93.54
As shown in Table 8, on the average results across three datasets, DyGRASP consistently outperforms LKD4DYTAG for both and AUC under both transductive and inductive settings. A more detailed analysis reveals the specific strengths of our method:
On datasets with less textual information like GDELT, where graph structure is more critical, LKD4DyTAG performs slightly better. This is likely due to its custom deetined base TGNN being more effective capturing structural features.
However, on text-rich datasets with complex semantic patterns like Googlemap CT. DyGRASP demonstrates significant lead. This validates design for modeling recent and global temporal semantics provides superior semantic reasoning capabilities compared to existing methods.
contribution: DyGRASP's explicit
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F CASE STUDY
We provide case study from the Googlemap_co dataset below, which demonstrates how the model uses global summarization to predict whether customer Joe Golin will visit the business Liquor Depot at timestamp 48264.
Global Summarization for Customer Node "Joe Golin" (at i 47960)
Joe Golin is customer who values exceptional service, high-quality food, and unique dining experiences, particularly Asian cuisine, seafood restaurants, and pubs. His enthusiasm for crab ragoons remains unwavering, with his positive reviews Toshi Izakaya, J Timothy's Taverne, and 707 Asian Fusion showcasing his love for this dish. He is also fan of imaginative waffles and great pizza, evident from his positive reviews of THE SLOPPY WAFFLE LLC and Pizzeria Marzano. While he is willing to try new restaurants, his reviews suggest that he has high expectations and can be disappointed they do meet his standards, as seen in his review of Taco Bell. Joe also values excellent service, as evident from his positive reviews of Black Diamond Body Piercing, Mulligan's 's O'Connor's Public House, and Onion Mountain Kitchen. His recent visits to Dos Amigos and Midas, where he experienced excellent service, further emphasize his appreciation for friendly and efficient service.
Description:
Global Summarization for Business Node "Liquor Depot" (at / = 42596)
Liquor Depot, Inc. is a reehy-rrcommeeded liquor store offering an extensive selection of beers, wines, and spirits competitive prices. Their knowledgeable staff provides excellent assistance in finding the perfect products, frequently featuring a wide range of bargain wines and an exceptional bourbon selection, including rare and hard-to-find options. The store continues to host events, such as the "Bourbon Bash," allowing customers to purchase limited-edition bottles. They boast an impressive selection of 12 packs, wine, and whiskey, catering to various tastes and preferences. The store is praised for its great prices, friendly staff, and beautiful store environment, making a go-to destination for liquor enthusiasts in Connecticut. The store's 's ability to adapt to changing tastes and trends, such as great St. Patrick's 's displays, further solidifies its reputation as a top choice.
Description:
By processing these global summaries, our model can identify strong semantic alignments between the user's preferences and the business's offerings:
Joe Golin's 's summarized interest in "pubs" directly corresponds with "Liquor Depot" being a liquor store.
Interest Alignment:
Service Quality Alignment: Joe's high value for "exceptional service" is matched by Liquor Depot's reputation for "knowledgeable" and "friendly staff" who provide "excellent assistance".
Joe's preference for "high-quality food and unique dining experiences" " aligns with Liquor Depot's "exceptional bourbon selection," " "rare and hard-to- find options," and "impressive selection," which constitute high-quality and unique product experience.
Unique Experience Alignment:
Based on this semantic reasoning, the model can infer a high probability that Liquor Depot meets Joe Golin's criteria, thus predicting his visit at the future timestamp. This illustrates how global summarization provides crucial, high-level context that goes beyond individual interactions to improve predictive accuracy.
G
USE OF LLM ASSISTANTS
We used LLM for the sole purpose of copyediting the paper improve its clarity and readability.
20


Reviews:
[{"review_id": "CKjHsJK03U", "reviewer": "Reviewer_hKYq", "summary": "This paper introduces DyGRASS, a framework designed to address semantic comprehension and reasoning in dynamic text-attributed graphs. The authors design a sliding window strategy that effectively captures short-term temporal patterns. Additionally, they employ an LLM to distill long-range, chain-structured semantic information at the node level, enabling comprehensive global semantic representation learning. Evaluations conducted on four benchmark datasets demonstrate the model's outstanding performance and its adaptability to different LLM and temporal GNN backbones.", "questions": "See above.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The inefficiency of LLMs and the limited semantic modeling capability of GNNs represent two major challenges in modeling DyTAGs. This paper offers an effective and well-founded solution to address these issues.\n\n2. The technical design of the proposed framework is well-motivated, and its effectiveness has been empirically validated through experiments.\n\n3. Comprehensive experimental results are provided, demonstrating the superior performance and efficiency of the proposed framework.", "weaknesses": "1. To better validate the efficiency of the proposed DyGRASS, a comparison of inference time and token consumption with other baseline models should be provided.\n\n2. Case studies illustrating the global summarization results of LLMs are recommended, along with an analysis of how global semantic information enhances prediction performance.", "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": "FA2kjYjc9Q", "reviewer": "Reviewer_tZWv", "summary": "DyGRASP tackles Dynamic Text-Attributed Graphs by combining large language model reasoning with temporal GNNs. It captures recent semantics via node-centric implicit reasoning and sliding windows, and global dynamics via explicit, prompt-guided summaries chained over time. Merging these signals with graph structure yields O(|E|) efficiency and accuracy, boosting Hit@10 by up to 34% and generalizing across LLMs and temporal GNN backbones.", "questions": "1. What is the training pipeline for the node-retrieval task—end-to-end or two-stage?\n\n2. What is the rationale for “sorting by timestamp” in node-centric implicit reasoning?\n\n3. In Eq. (2), the formulation seems to capture local rather than global semantics; please clarify how global semantics are obtained.\n\n4. What do “gathering” and “aggregating” features mean in $MP_{G}(\\cdot)$, which typically refers to the TGNNs? What is the difference between $MP_{G}(\\cdot)$ and TGNNs' neighbor aggregation?\n\n5. Could you please compare the proposed method with existing dynamic text-attributed graph approaches?\n\n6. Could you please evaluate the effectiveness of the sliding window?\n\n**Major Question:**\n\n**(1) Empirically, DyGRASP (DyGFormer) with both global and recent modules improves performance by only ~2% over DyGFormer alone in Tables 1–2, yet removing “global and recent modules” in Fig. 4 causes a >10% drop. Could you please explain this discrepancy?**", "limitations": "", "rating": 2, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. The research topic is interesting and practically relevant, particularly the node retrieval task.\n\n2. Efficiency is a critical issue, especially when employing LLMs as encoders.", "weaknesses": "1. There are multiple prior works on dynamic text-attributed graphs; however, the paper’s motivation is framed against static TAGs, which mispositions the contribution.\n\n2. The related work is outdated—particularly for Temporal GNNs. The most recent citations end in 2023; please include the latest works.\n\n3. The description of the “global reasoning chain” is unclear; stating it is “RNN-like” is insufficient and difficult to follow.\n\n4. Although several dynamic TAG methods are cited, the paper does not compare against them in the evaluation.\n\n**Major Concern:**\n\n**(1) Empirically, DyGRASP (DyGFormer) with global and recent modules improves performance by only ~2% over DyGFormer alone in Tables 1–2, yet removing “global and recent” in Fig. 4 causes a >10% drop.**", "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": "RshBtDb4N8", "reviewer": "Reviewer_amvQ", "summary": "This paper proposes an innovative framework named DyGRASP for the semantic understanding and reasoning problems of dynamic text attribute graphs. Targeting the insufficient semantic understanding ability of traditional temporal graph neural networks and the low efficiency of large language models, the authors propose a sliding window mechanism to efficiently capture recent temporal semantics, and use LLM to summarize the chain-structure long-range semantics of nodes for global information modeling. Experimental results on 4 benchmark datasets showcase its performance superiority and the generalization ability to various LLMs and temporal GNNs.", "questions": "None", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "S1. The paper is well-written, with clear and engaging prose. The figures are well-crafted and greatly facilitate the understanding of the proposed framework's technical details.\n\nS2. Addressing the targeted challenges, namely, LLM efficiency and the modeling of global semantic evolution in dynamic text, is essential to the DyTAG framework. Each component of the proposed design is carefully motivated and well justified.\n\nS3. Extensive experimental results are provided, including the transductive & inductive performance, ablation and hyper-parameter study, as well as efficiency study, providing a comprehensive understanding of the effectiveness of the proposed framework.", "weaknesses": "W1. Several case studies are provided to better illustrate the reasoning process of the proposed framework, such as the summarization results generated by large language models.\n\nW2. The authors employ a uniform partition strategy for global semantic summarization; however, since user intent may fluctuate over different time periods, this approach could be considered a technical limitation of the framework.", "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": "qnFWlcux8i", "reviewer": "Reviewer_jwom", "summary": "The paper addresses the challenge of Dynamic Text-Attribute Graphs (DyTAGs), which feature time-evolving graph interactions and associated text attributes that are common in real-world applications. Existing methods, like Graph Neural Networks (GNNs) and Large Language Models (LLMs), mainly focus on static Text-Attribute Graphs (TAGs). Extending these approaches to DyTAGs is difficult because they fail to account for recent-global temporal semantics, which include both the recent semantic dependencies among interaction texts and the global semantic evolution of nodes over time. Additionally, applying LLMs to the vast and evolving text data in DyTAGs introduces efficiency problems.\n\nTo solve these challenges, the authors propose a novel method called Dynamic Global-Recent Adaptive Semantic Processing (DyGRASP). DyGRASP combines LLMs and temporal GNNs to reason on DyTAGs in an efficient and effective manner.", "questions": "Authors may consider providing more analysis about the efficiency of the framework.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. DyGRASP effectively combines both recent and global temporal semantics by utilizing sliding window mechanisms for short-term dependencies and RNN-like structures for long-term dependencies. This dual approach improves the model's ability to capture evolving patterns in DyTAGs.\n\n2. The combination of LLMs for semantic processing and temporal GNNs for structural reasoning is an effective solution for addressing both the textual and graph-based challenges in DyTAGs. The model’s design optimizes the use of LLMs to handle the evolving text efficiently, solving a major bottleneck in current systems. The idea of implicit and explicit reasoning paths may provide some insights to the researchers in this field.\n\n3. This paper provides a comprehensive experimental result to verify its effectiveness.", "weaknesses": "1. While the integration of LLMs and temporal GNNs is innovative, it also increases the overall complexity of the model. This dual integration might result in higher computational costs and more difficult tuning for large-scale applications.\n\n2. The paper highlights the efficiency of DyGRASP in handling evolving texts, but it would benefit from a more in-depth discussion of the computational trade-offs involved in its design, particularly when applied to large-scale DyTAGs with vast amounts of dynamic text.", "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)