GraphMaster: Automated Graph Synthesis via LLM Agents in Data-Limited Environments
Authors: Enjun Du
Xunkai Li
Tian Jin
Zhihan Zhang
Rong-Hua Li
Guoren Wang
Venue: NeurIPS 2025 spotlight ()
Keywords: Graph data synthesis
text-attributed graphs
multi-agent framework
large language models
retrieval-augmented generation
data-limited environments
Primary area: deep_learning
TL;DR: GraphMaster is a collaborative LLM multi-agent system that synthesizes semantically rich, structurally coherent text-attributed graphs to overcome data scarcity and empower Graph Foundation Models.

Abstract: The era of foundation models has revolutionized AI research, yet Graph Foundation Models (GFMs) remain constrained by the scarcity of large-scale graph corpora. Traditional graph data synthesis techniques primarily focus on simplistic structural operations, lacking the capacity to generate semantically rich nodes with meaningful textual attributes—a critical limitation for real-world applications. While large language models (LLMs) demonstrate exceptional text generation capabilities, their direct application to graph synthesis is impeded by context window limitations, hallucination phenomena, and structural consistency challenges. To address these issues, we introduce \textbf{GraphMaster}—the first multi-agent framework specifically designed for graph data synthesis in data-limited environments. GraphMaster orchestrates four specialized LLM agents (Manager, Perception, Enhancement, and Evaluation) that collaboratively optimize the synthesis process through iterative refinement, ensuring both semantic coherence and structural integrity. To rigorously evaluate our approach, we create new data-limited “Sub” variants of six standard graph benchmarks, specifically designed to test synthesis capabilities under realistic constraints. Additionally, we develop a novel interpretability assessment framework that combines human evaluation with a principled Grassmannian manifold-based analysis, providing both qualitative and quantitative measures of semantic coherence. Experimental results demonstrate that GraphMaster significantly outperforms traditional synthesis methods across multiple datasets, establishing a strong foundation for advancing GFMs in data-scarce environments.

GraphMaster: Automated Graph Synthesis via LLM Agents in Data-Limited Environments
Enjun Du', Xunkai Li", Tian Jin', Zhihan Zhang', Rong-Hua Li Guoren Wang' 'Beijing Institute of Technology
The Hong Kong University of Science and Technology (Guangzhou)
Abstract
The era of foundation models has revolutionized Al research, yet Graph Foundation Models (GFMs) remain constrained by the scarcity of large-scale graph corpora. Traditional graph data synthesis techniques primarily focus simplistic structural operations, lacking the capacity to generate semantically rich nodes with meaning- ful textual attributes critical limitation for real-world applications. While large language models (LLMs) demonstrate exceptional text generation capabilities, their direct application graph synthesis is impeded by context window limitations, hallucination phenomena, and structural consistency challenges. To address these issues, we introduce GraphMaster the first multi-agent framework specifically designed for graph data synthesis in data-limited environments. GraphMaster orchestrates four specialized LLM agents (Manager, Perception, Enhancement, and Evaluation) that collaboratively optimize the synthesis process through iter- ative refinement, ensuring both semantic coherence and structural integrity. To rigorously evaluate our approach. we create new data-limited "Sub" variants of six standard graph benchmarks, specifically designed test synthesis capabilities under realistic constraints. Additionally, we develop novel interpretability assess- ment framework that combines human evaluation with a principled Grassmannian manifold- basddd-bbddddd analysis, providing both qualitative and quantitative measures of se- mantic coherence. Experimental results demonstrate that GraphMaster significantly outperforms traditional synthesis methods across multiple datasets, establishing a strong foundation for advancing GFMs data-scarce environments
Introduction
In the era of foundation models, unprecedented advances in natural language processing 59 and computer vision [14 48 15 26) have been enabled by massive training corpora (57, 55
58 56, 66
52 [].
Graph Foundation Models (GFMs) 31 28 59 S represent a promising frontier for Al in graph-structured data, yet their development faces critical bottleneck: the scarcity of large-scale, diverse graph datasets, Unlike text and image domains where data collection is relatively straight- forward, gathering and annotating graph data often requires specialized expertise and significant resources. This data quantity constraint has become the primary challenge for training robust GFMs, particularly model size increases and demands exponentially more training examples for optimal performance.
Graph data synthesis offers a strategic solution to this fundamental constraint by automatically generating new graph samples that maintain both semantic richness and struetural validity. Existing synthesis approaches, however, face substantial limitations. Edge-level operations [63 65 manipulate
Corresponding author
"Code is available on htttp::///gituuu..om/////uuuuuuu
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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existing connections but cannot create novel nodes or patterns. Node-level mixing techniques like GraphMixup [53] generate synthetic nodes by interpolating features but often produce semantically inconsistent attributes, particularly with textual features. Graph-level synthesis methods such as G-Mixup 18 create entirely new graphs but struggle balance global structure with local semantic coherence. The core limitation across these traditional methods is their inability to simultaneously preserve meaningful semantics while generating structurally valid expansions- deficiency particu- larly pronounced when handling text-attributed graphs (TAGs) where both connectivity patterns and textual node features must remain coherent.
Large language models have demonstrated remarkable capabilities in understanding and generating text [19 11 61 24 [2 (5), suggesting potential for synthesizing text-attributed graphs. However, directly applying LLMs encounters several critical challenges: standard context windows cannot process entire graphs with numerous textual nodes [3]; L.LMs excel at semantic understanding but struggle to maintain structural consistency [22 and without proper coordination, they tend to produce inconsistent or hallucinated content that fails capture the intricate balance between topology and semantics [3 Furthermore, in realistic scenarios with limited available data, LLMs have insufficient examples to learn complex graph patterns 227 25
To address these challenges, we propose GraphMaster. a novel multi-agent framework specifically designed for graph synthesis in data data-limited environments. GraphMaster decomposes the complex synthesis task into specialized sub-tasks handled by four collaborative LLM-powered agents, each targeting specific challenges: (1) The Manager Agent coordinates the overall process and determines optimal synthesis strategies based on current graph characteristics, orchestrating the complex synthesis workflow; (2) The Perception Agent analyzes graph structure and employs advanced sampling to identify representative subgraphs processable within LLM context constraints, directly addressing the context window limitations; (3) The Enhancement Agent generates new nodes and edges with consistent semantics and structure, mitigating hallucination by maintaining coherence with existing graph elements; and (4) The Evaluation Agent assesses quality based on both semantic coherence and structural integrity, providing feedback for iterative improvement to ensure structural and semantic consistency. This decomposition enables targeted solutions for each challenge that a single-pass LLM approach cannot address.
Through this collaborative, iterative process, these specialized agents overcome the limitations of both traditional methods and direct L.LM applications. The multi-agent architecture enables Graph to effectively balance semantic richness with structural graph data even with limited training examples. By introducing modular reasoning (through task decomposition), semantic control (via specialized agent expertise), and iterative optimization (through feedback cycles), GraphMaster achieves synthesis capabilities beyond what single-pas. pass approaches can deliver.
high-quality synthetic
Our contributions can be summerized as follows:
New perspective for LLM-based TAG Synthesis: we first propose a novel multi-agent framework from the RAG perspective to synthesize TAG under data-limited environment. By integrating context retrieval with iterative feedback, this new perspective enables both semantic richness and structural fidelity.
Groundbreaking Benchmark:
We introduce standardized "Data-limited" variant testbed for text-attributed graph synthesis and develop a -ura--pereecctive interpretability assess- ment-combining expert human evaluation with Grassmann manifold-bai -bsed analysis-to provide reproducible comparisons and deep semantic-structural insights.
State-of-the-Art Performance: Extensive experiments on multiple datasets and GNN architectures demonstrate that our method consistently outperforms existing baselines, setting a new benchmark for data- limited TAG synthesis.
2 Background Methods
2.1 Classic Graph Data Synthesis Methods
Traditional graph data synthesis methods [7] address data searcity through various approaches. Edge- level operations (63 modify topology by adding deleting connections. Node-level techniques like

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GraphSMOTE 62 generate new nodes through minority class interpolation. Graph-level methods
such G-Mixup 8 create entirely new graph instances via graphon interpolation. Interpolation
based approaches 40 39 combine hidden representations to enhance model robustness. Despite their diversity, these methods primarily focus on structural manipulations without generating semantically meaningful textual attributes.
2.2 L.MM-based Multi-Agent Systems for Data Generation
Recent L.MM-poered multi-agent frameworks demonstrate capabilities complex data generation tasks. General collaboration systems like Self-Instruct [4] and distributed simulation platforms 34 establish architectures for coordinated AT systems. graph contexts, approaches like GoG and LLMM-based social simulations [20] leverage semantic understanding for graph-related tasks.
However, specific applications for text-attributed graph synthesis in data-limited environments remain largely unexplored.
2.3 Problem Formulation: Graph Data Synthesis
Text-Attributed Graphs. We formally define text-attributed graph (TAG) as (V, &, X.)). where V= = {u 22 ..... UN is set of N nodes, & _ V x V is the set of edges with corresponding adjacency matrix E {0, N X'' {I1, ,., NNN is the set of textual features with each a; corresponding node 0; V. and y {y1, 22. . , represents the set of node labels where y/ E (1,2,..., C) for C distinct classes.
Knowledge Extraction. Given the context length constraints of LLMs, we define knowledge extraction function + 9 - K. that samples representative subgraph as:
K + (Vx: Ex. Ny. D4),
where V4 C & {(u,, 0y) E v. U, Vi ), and Xx, V4s the corresponding text attributes and labels. The extraction function employs specialized sampling strategies to ensure IC captures both structural and semantic characteristics of G while remaining within LLM context limits.
Graph Synthesis Process. The graph synthesis process formalized as a function V -> G, that generates new graph elements based on the extracted knowledge:
G. (XC) = (Va, &s, Ks, 3/),
where G. represents the synthesized graph components. Function is implemented through our framework that encompasses both semantic understanding and structural pattern recognition.
Graph Synthesis. The final enhanced graph merges the original and synthesized components:
Goow G G. UVUV, UE, EU,,, U & XU
where & = ((r,, v1) U; E V.u, E v.} represents newly created edges connecting original and synthetic nodes. The quality of Gnew is evaluated based on both semantic coherence (how well X, aligns with original textual patterns) and structural fidelity (how well E, and E, preserve the topological characteristics of 9).
3 The Proposed Method
We present GraphMaster, a multi-agent framework conceptualized through the lens of Retrieval- Augmented Generation (RAG) (50 54
to address the challenges of graph synthesis data- constrained enviromments. As illustrated in Figure GraphMaster implements a hierarchical RAG paradigm wherein four specialized LLM--poered agents operate collaboratively in recursive optimization loop to generate semantically rich and structurally coherent graph extensions.
3.1 Framework Overview: RAG-Based Multi-Agent Architecture
GraphMaster formalizes graph synthesis as an iterative RAG process, operating through specialized agents in closed-loop optimization system. While single LLM might possess the theoretical
3

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Manager Agent
diu RECIUAMS
A
Merged report
POO
convergence?
Structure
Semantics
Repert
Agent mitiaizatioo
mede chocse
Semantic
Topological
Report
Filter nodes with qudlified quality
Querr
Reserved Nodes
Kampas Vo (LMMei Re,
Evaluation Agent
Perception Agent
Enhancement Agent
Reteiever
P( te &
Sementic-aeore
Commurity Ideniifidation
Mode-adeepivgg
base- Subgraph
Extraction
sompling
Care-Subgraph
Taken generation
Edge ginercttio
Nodes creation
New nodea
Senercte
prompt
onvironmental report
Environmental Report
Pix
PI K.A.
Figure 1: GraphMaster: hierarchical multi-agent framework for text- eext-attributed graph synthesis.
capability to understand graph structures, the inherent complexity of generating coherent graph data necessitates specialized multi-agent approach for three critical reasons. First, real-world graphs substantially exceed typical LLM context windows, requiring strategic sampling and knowledge extraction. Second, maintaining structural consistency across generated elements demands focused attention on connectivity patterns that single-pass generation cannot guarantee. Third, controlling hal- lucination requires continuous evaluation and refinement through iterative feedback. A collaborative multi-agent architecture effectively addresses these challenges through specialization and integration:
Gnew = RR ( Q R, Aretrieee Agenerate: A
where G is the original graph, Q represents the query formulation (enhancement mode), R. denotes the retrieval strategy, and Arerieeee Agenerae. and Aevalunte correspond to the agent-specific functions for retrieval, generation, and evaluation, respectively. In each iteration, the Manager Agent formulates the query to guide the synthesis process, the Perception Agent retrieves relevant context to overcome con- text window limitations, the Enhancement Agent generates new content while maintaining structural consistency, and the Evaluation Agent assesses quality and addresses potential hallucinations with this cycle continuing until convergence. This collaborative framework enables each agent to focus on a specific challenge while collectively producing coherent graph extensions that a single-pass approach cannot achieve.
3.2 Manager Agent: Query Optimization and Control Mechanism
The Manager Agent serves as the meta-cognitive controller that formulates the synthesis query Q at iteration based on comprehensive environmental status report R, = (G.) generated by the Perception Agent. The mode selection function formalized as: M, aLMMs (R,) e (semantic, topological) } where LLM represents the Manager Agent's 's reasoning process that ana- lyzes community structures and label distributions captured R2 This query formulation implements an adaptive mechanism where the Manager optimizes a multi-objective utility function:
w = arg max (A1 Usemlws Gi) + Suustru G.) + AsUbal Gt) WEB
(5)
where Ω is the strategy space, Uwe. Usinet, and Uhai represent semantic coherence, structural in- tegrity, and class balance utilities respectively, with adaptive weights X; that evolve according to: x = X + >,, P(G,) where P(G.) measures synthesis progress and r is a learning rate. The Manager orchestrates state transitions, modeled 8141 T(st, az, Mc), where states 8, reflect graph composition and actions a, E {ap, ag, av correspond to agent invocations for Perception, Enhancement, and Evaluation respectively.
3.3 Perception Agent: Context-Aware Corpus Retrieval
The Perception Agent implements the retrieval component of the RAG paradigm, extracting a relevant subgraph from the input graph G, based on the query Qr = Mc. This retrieval process is formalized

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as: K, = = Q1) (Vk. &g. 3%) where Kr represents the retrieved knowledge capsule. The retrieval function A operates through three sequential stages:
Semantic-aware Community Identification: The agent employs a semantic-enriched modularity maximization algorithm calculate the community distribution of semantic associations for TAG:
kiky
dsem (Iis, Fzj Σi,m dsem (Di, Fma)
Qsem
Σ 8
Atj
(1 -
8(c),
2m
2m
where dsem (24, xzy)
x. xg ||x xx computes semantic similarity between node attributes, k; and ky
represent the degrees of nodes and j, and y balances topological and semantic factors.
Mode-Adaptive Seed Selection Strategy: Based on the enhancement mode M,, the agent selects an optimal seed community Cs:
arg min [Cil- (1 + p Var (z, C.))), if M, = semantic,
C.
foz Varin Mj arg max (umbal
if M₁ topological
where (imbal maxe' [Vel/VVll quantifies class imbalance, Var denotes the variance of node textual features within community and l weights semantic variance importance. For semantic synthesis, the smaller communities with low internal semantic variance are prefer to be selected to establish a cohesive foundation. For topological synthesis, nodes from minority classes are prioritized.
Hierarchical Stochastic Diffusion Sampling: The agent employs a mode-conditional Personalized PageRank (PPR) algorithm, defined as (k+1) = ov + (1 a a)WT (*), where the teleportation vector t varies by enhancement mode: e [2.l v; E C. and M, = semantic,
1
Σ, 4w,=
E Virain and M, = topological, where ý = arg min| {u, E Vuain y = c} identifies the label with minimal representation. Following diffusion convergence, the final knowledge subgraph is selected as:
KG = {u, E Stop-% in min(1, B T,/ ///maxx , n diverse
(8)
where |Kel N is constrained by the LLM context window, Stop contains the top K% nodes by PPR score, Yú ~ Uniform(O, 1) introduces controlled stochasticity, and Saiverse ensures community coverage. The environmental status report R. encapsulates multi-scale graph properties:
R₂ (elobhal (Petass IC (Pcomm im1 Dstructs Daem
where Pglobal captures global statistics, Pêtass and Pcomm encode class-level and community-level properties, while D Dammat and Dsem represent structural and semantic distributions.
3.4 Enhancement Agent: Context-Condiinn Conditioned Generation
The Enhancement Agent implements the generation component of the RAG paradigm, synthesizing new graph elements (no more than M% of the knowledge subgraph) based on the retrieved knowledge and environmental report. The synthesis process follows Eq. (2) where K. = (K², Rr, M2). For
semantic mode, the LLM generates node attributes using a conditional autoregressive model:
P(z, KC) = II 2 Xr, KC) 1=1
(10)
where 2f. is the i-th token of attribute and L is the sequence length. This formulation enables the LLM to generate coherent textual attributes that maintain consistency with the knowledge subgraph while introducing appropriate variations.
5

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Crucially. regardless of the current enhancement mode, the agent always generates both node attributes and their connections. For topologiaal mode, the LLM models edge connections between new node v and existing nodes by estimating the probability:
P((U,) v.) Ec]KC = o (01-siin sisssmm(ss, x4)) + 82- (W()s) (Nx((.)) + 03 [K((1))
(0)
K max, k.
(11)
where M(v)) is the neighborhood of 14 (v,) represents neighbors of v2 in the knowledge subgraph, and a is the sigmoid function. The coefficients {0,} are dynamically adjusted based on the query mode Mr. This dual-mode generation enables GraphMaster to adaptively emphasize either semantic coherence or structural fidelity while maintaining integrity across both dimensions.
3.5 Evaluation Agent: Multi-dimeenional Quality Assessment
The Evaluation Agent implements a comprehensive verification mechanism that integrates four critical information sources:
2 LLML (Ro, R2, K,, G.
(12)
where 2, represents the quality assessment outcome, Ro is the initial environmental report serving as a baseline, R, is the current environmental report, Kr, is the retrieved knowledge, and G is the newly synthesized data. The Evaluation Agent simultaneously assesses two key dimensions: (i) Semantic Coherence: Evaluates whether the generated textual attributes are contextually appropriate, domain- consistent, and meaningful within the graph's 's thematic scope. (ii) Structural Integrity: Assesses whether the new edges form logical connections that preserve the original graph's 's topological patterns while addressing structural gaps.
For each generated node vs E V4, the LLM computes a composite quality score, with the final accepted node set defined as:
V necepted = [v, E V LLMv Ro, R2, Kc.) > Tr}
(13)
where the threshold Tr is adaptively updated: Ti Ti-1 ((F(w))) - with F((tt) representing the average quality score at iteration t. The convergence determination employs temporal quality gradient analysis:
Converged, = (1( max [F(wet) Fr-s(4ū-))) < <EA LLL (Ro, ( R.) = True (e(......))
(14)
where IT(-) is the indicator function and L. assesses whether synthesis objectives have been achieved. If convergence is detected, the Evaluation Agent signals task completion to the Manager Agent; otherwise, it triggers another iteration of the synthesis process
3.6 Time Complexity Analysis
The time complexity of GraphMaster is dominated by three operations: (1) community detection and PPR computation in the Perception Agent, which run in near- near-linear time O(VV + [E,]) on the current graph; (2) LLM inference for node attribute generation and edge probability estimation, which scales with the size N of the retrieved subgraph rather than the full graph; and (3) quality assessment, which evaluates fixed number of newly generated nodes against predetermined criteria. Since N is constrained by the LLM context window and typically small relative to 1%l. the LLM operations remain efficient regardless of overall graph size. If the iterative process runs for T iterations before convergence (generally small due to the Evaluation Agent's ' stringent criteria), the overall complexity is T times the per-iteration cost. This architecture enables GraphMaster to scale effectively by leveraging LLMs for semantic generation on bounded contexts while using efficient graph algorithms for structural computations.
'e compares GraphMaster with recent remarkable graph data synthsis methods in Appendix Theoretical analysis of agent capabilities given in Appendix

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4 Experiment
To evaluate GraphMaster comprehensively, we formulate four research questions: (RQI): Can
GraphMaster generate high-quality text-atrributed graph data in data-limited environment? (RQ2):
Can the graph data synthesized by GraphMaster retain the original graph features well? (RQ3): Can GraphMaster maintain interpretability well? (RQ4): What is the relative contribution of each component in GraphMaster to the overall synthesis quality?
4.1 Overall Performance (RQ1)
evaluate GraphMaster's ability to synthesize high-quality graph data by applying it enhance the data-limited datasets we created and assessing whether the enhanced datasets improve downstream model performance. We employ standard metries including Accuracy and F1 Score evaluation criteria, with higher values indicating superior performance.
We
Baselines and Datasets. The comparative baselines are categorized into five groups: (1) original
TAG training without data synthesis; (2) Classic data augmentation methods: GAugO [6]] : (3) LLM-
based data aigmentation methods: GraphEdit [16] and LLM4RGNN 600; (4) Classic data synthesis
methods: GraphSmote 62). G-Mixup [18), IntraMix [64), Graph Adasyn (29 FG-SMOTE [42] and
AGMixup 330; (5) LLM-based data synthesis methods: GAG (2 and LLM4NG [TT] noting that there are very limited baselines for TAG synthesis using LLM, and we created these two additional baselines named Mixed-I.LM and Synthesis-L.LMM, whose implementations can be found in the Appendix B Our experiments utilize six widely recognized text-attributed graph datasets: Cora 32) Citeseer [13], Wikies [TO], Arxiv2023 [36], and History and Children (49. is worth noting that in order to better simulate the data-limited environment to test the effect of data synthesis, we created 6 data-limited datasets, namely SubCora, SubCiteseer, SubWikics, SubHistory, SubArxiv2023, and SubChildren (details are given in Appendix C.. In this article, unless otherwise specified, we assume that the augmentation- based method uses the original dataset, while the synthesis based method uses the data- limited dataset we created. For downstream task evaluation, we implement four established graph neural network architectures: GCN [22,, JKNET [46], GraphSage [77 and GAT 38).
Implement Details. We ran the entire experiment on eight 80G A100 GPUs, using the QwQ-32B model (37 as the base LLM and enabling it to assume different agent roles through iterative calls. For the background knowledge nodes, we set N 30, and for the newly generated nodes, we configured M% = 15% (The hyperparameter selection analysis are given in Appendix E. In training the GNN model, we first initialized the text attributes with Sentence-BEi before proceeding with training. To ensure the robustness of our experiments, we repeated each experiment 50 times and reported the mean and standard deviation of the results.
[35 to generate the initial features
Table 1: Comparison of GraphMaster with other TAG synthesis methods in GCN model. Best performance is indicated by the bold face numbers, and the underline means the second best. 'Acc' and 'FI' are short for Accuracy and F1 Score, respectively.
Tygge
Model
Asz
Cor3
Cilssss
Hittoy Acc
Andr2022
Aux
Chidtron
Original
Original Modii
88.5
130003
70
84.2306
5491005
$2.61077
32.311.5
Clasic-Au
GAuzO
88.108
88.06110
761:207
77.2003
79.91699
77.7=07
$30500
$1,5206
33.640.7
11M-Aa
LLM-RGNN
91.2-0.
80.9213
76.6111
53.621.4
$89600
79.361.1
10.1004
55.7+14
367460.8
H0TEET3
78
505500
15.25505
G-Micup
$..41.11
87.0107
76.2:09
76.8 1.
99 70.04
780008
$4.600
286104
565:03
$3.0006
33.016.3
Clasic- Syn
GrophAdayn
IntaMii
59 1M22-033
50.10.
82.8.007
38. 730.5
7859-11 11
7133:08
70.22 70:55
78 4.444
S0N61.2
73.711.
745500
78
$6.611
$244:.5
42. 42.7
46 1208
77 71.5607
72.41.1
S7.66111
53.900
45:2:09
5355915
30.110.7
330000
FG- RG-SMOTE
5..0115
87.611.
75.7-08
74. 1.
$1.010.5
7000:10
$5.011
440:0.
764212
558814
33.1115
33.3669
AGMisus
$4.7-04
25.540.4
71.7-09
73 1-211
7886009
766:1.1
$1.8609
42.-0.
76841.1
53.5505
$3.6110
32,660.
TTAC
31.110
119111
K1 107
193 1X
750103
LLMING
15.20.3
94.02002
73
720::03
7281001
729
$2.
79.0 10.
=02
LLM Syn
Mixed
61
39,66077
$48104
623:05
000110
GraphMaster
957511
92.5.1.
883:00
87.7:1.1
879198
863-09
9266333
634:14
87.9313
663:13
058:14
47.8133
Results. As shown in Table to space limitation, other three models' results are given in Appendix F), GraphMaster consistently outperforms all baselines, demonstrating the superiority of our approach. Notably, we observed that some baseline methods even yield lower performance than the original dataset. This is primarily because traditional graph synthesis techniques fail to capture the semantic nuances of sentences; consequently, when using Sentence-BEr BERT embeddings
7

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instead of Dag-of-words representations, their effectiveness is significantly diminished. Moreover, the other LLM- -baedd baselines we compared against mainly focus on anti-interference detection or data synthesis on other scenarios rather than TAG data synthesis, resulting in their performance being significantly lower than that of Graph, which targets TAG data synthesis. Finally, the two L.IM- based TAG synthesis baselines we developed show significant advantages over traditional baselines. However, since they cannot fully understand the semantics and topological structure of TAG, although they are higher than other baselines, they are still significantly lower than GraphMaster" M
4.2 Synthetic Graph Feature Analysis (RQ2)
Orignal Gropt
100
Madio Degiee
Node Degnns
Node Uabel
(a) Degree distribution
(b) Clustering coefficcient
(c) Label homogencity
Figure 2: Graph feature analysis on Children dataset.
Our second research question examines whether the new graph data generated by GraphMaster in environments can maintain consistency with the original graph's structural features. We conducted a comprehensive analysis across three dimensions: degree distribution, clustering coefficient, and label homogeneity. As shown in Figure 2 GraphMaster demonstrates excellent performance in preserving the network's topological backbone. For example, the two-sample Kolmogorov-Smmimoy test statistic between the degree distributions of the original and synthesized Children graphs 0.357 (p = 0.059), indicating no statistically significant difference. The clustering coefficient similarity score is 0. 0.835, which represents a substantial improvement over the original lata-limited Children graph (0.785). Concurrently, the label homogeneity similarity reaches an impressive 0.988 (the heatmap of label-label connection frequencies is almost identical for original vs. synthetic), indicating minimal differences in class mixing patterns. These characteristics show that GraphMaster can generate high-quality synthetic graphs that retain key structural properties of the original data. (Additional graph comparison figures are provided in Appendix G
4.3 Interpretability Analysis (RQ3)
To evaluate the transparency of our GraphMaster model, we conduct both human-centered and algorithmic assessments of interpretability (theoretical details in Appendix H). For human evaluation, 50 expert reviewers rated 200 synthesis instances across three dimensions: process transparency, decision justification, and outcome predictability. The overall Traceability Score quantifies how well humans understand the generation process:
1 R.N
Tecure
triis
(15)
where fr.; represents the score given by reviewer for instance i. In parallel, we leverage a Grassmann manifold-based approach to systematically assess semantic consistency of synthesized nodes. This mathematical framework provides principled way to measure how well generated nodes align with the semantic direction of background knowledge, yielding coherence scores in the range [o. 1].
Our human evaluation results demonstrate that GraphMaster exhibits excellent interpretability, with an average traceability score of Teoor = 0.92, significantly outperforming Mixed-L..M (0.66) and Synthesis-LLMM (0.59). For the Grassmann manifold- -andeeld-based evaluation method, Figure 3a shows that GraphMaster significantly outperforms comparative methods in terms of semantic coherence,
"Case study are given in Appendix K
8

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PMD
1000
6
Dimm Tauablity
(a) Semantic Score Distribution
(b) Cross-Dataset Comparison (c) Hlmann-Algrriin Agreement
Figure 3: Interpretability Analysis of GraphMaster.
indicating that GraphMaster can generate nodes highly aligned with the principal semantic direction. The score distribution more concentrated in higher value regions also indicates stronger consistency in the quality of generated nodes. Figure 3b shows that GraphMaster maintains high performance across all datasets, consistently exceeding the interpretability threshold of 0.7. Figure 3c demonstratess strong correlation between human ratings and semantic coherence scores (r=0.78, p=0.00001), further validating our Grassmann manifold-based approach as an effective metric for measuring interpretability. This alignment between human judgment and geometric measures confirms the practical relevance and feasibility of our mathematical framework.
4.4 Ablation Study (RQ4)
Table 2: Ablation experiment in GCN model.
model
Apc
Corn
Fl
Apc
Caeseer
Fl
Ace
Wikess
Fl
Acc
Hissoyy
F
Apc
Arsi
C1
Ap
Chikkrnn
Fl
UWt--22
R3.7 2 92.
88.
110
$7.7
=
N7.9
36.8
V2
634
14
57
MC
47.
Qum-332
91.++009
0.9
0.9
86.5
85.4
85.7
84.2
0.0
80
60.8
0.0
64.2
1.1
16551 +
1.1
45.0
1.0
DeepSeck R1-32B
9..8
90.6
86,8
0.9
836
64.
1.2
66.9
1.2
46.1
L1aMA-338
91.1 0..
638
66.2
45.7
Perception Agent
68.5 0.6
0.7
57.0
Evalluazon Agest
89.6 0.7
62. 1.1
649
91.5
843
900
0.8
S1.1 107
66.3
N-30
N7.7
87.9
634
87.9
66.
68.8
47.8
Na40
92.0 00.8 0.0.
36.7
85.7
85.8
0.7
00.3
61.0
85
64.
1.4
66.7
66.0
M-10%
91.7 0.9
90.5
0.9
86.6
=
0.7
85.5
55.7
84.4
0.7
90
608
640
66.6
45.8
M-158
837 + 1.0
02.5 + 1.0
88.3
0.9
87.7
+
1.1
87.9
0.5
868
0.9
92.6
1.3
63.
1.4
K7.9
1.3
66.3
68.8
1.4
47.8
1.3
M-20%
9L. 90.1
1 0.8
86.2
0.6
S5,
0..8
85.3
1
0.5
50.0
0.6
90.0
077
60.5
0.
SL8
1
0.6
63.5
0.6
66.0
0.7
45.3
0.5
In this section, we investigate the relative importance of various components within the GraphMaster framework and their impact on synthesis quality. We systematically analyze how different agent configurations affect the overall performance. We selected Qwen-321 [I]. Decpseck-R1-32B (5) and Llama-33B 44, three models with parameters around 32B, as comparison models. Additionally, we examine how varying the size of the background knowledge base (N = [C] and the percentage of newly generated nodes (M%) influences synthesis effectiveness. We trained the model using GCN on six datasets, and the results are presented in Table Our findings indicate that the performance varies significantly across different LLMs, with QwQ-32B consistently outperforming the alternatives by 1.5-2.3% across all datasets. Notably, DeepSeek-R1-33 1-32B achieves the second-best performance despite ILLMMA333 having more parameters, suggesting that model architecture and pre-training approach are more critical than raw parameter count for this task.
The ablation results reveal that removing either the Perception Agent or Evaluation Agent substan- tially degrades performance (by 5.2% and 4.1% on average, respectively), with the Perception Agent proving particularly crucial. This confirms that both specialized components play essential roles in maintaining generation quality and cannot be omitted from the framework. Regarding hyperparame- ters, we observe that N 30 consistently outperforms both smaller (N 20) and larger (N 40) knowledge bases across all datasets. Similarly, setting M = 15% yields optimal results compared to 20%. These findings demonstrate that while sufficient context necessary
both M 10% and for high-quality synthesis, excessive background knowledge can dilute the model's 's focus. Likewise, generating too many nodes simultaneously reduces overall quality due to limitations in the model's generative capacity when handling multiple interdependent elements.

---

Conclusion
In this paper, we introduced GraphMaster, the first multi-agent framework for text-attributed graph synthesis that successfully addresses the critical bottleneck of data scarcity in training GFMs. By orchestrating specialized LLM agents in hierarchical RAG paradigm, our approach systemati- cally overcomes the limitations of traditional synthesis methods, generating semantically rich and structurally coherent graph extensions even severely data-constrained environments. Beyond the framework itself, we created specialized data-limited variants of six standard graph benchmarks and developed a novel dual-perseective interpretability assessment methodology that combines expert hu- man evaluation with theoretically grounded Grassmannian manifold- -ass- analysis. Comprchensive experiments demonstrate GraphMaster's 's consistently superior performance across diverse datasets and downstream GNN architectures. Future work could explore multi-scale synthesis approaches that simultaneously model global topology and local semantics, knowledge transfer mechanisms from data-rich to mata-limited domains, and adaptive sampling strategies optimized specifically for synthesis objectives. This work not only provides an immediate solution to the graph data scarcity problem but also establishes foundational methodologies for advancing interpretable graph data synthesis.
Acknowledgements
This work is supported by the NSFC Grants U2241211, 62427808 and U24A20255.
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NeurIPS Paper Checklist
1. Claims
Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Question:
Answer: [Yes]
Justification: In the abstract and introduction, we described in detail the applicable scenario of our model text attribute graph data synthesis based on large language model. We also emphasized our contribution the first RAG-based multi-agent TAG synthesis method, and proposed new data- datasets and a manual and Grassmann Manifold-bard semantic dual-view interpretability evaluation method. This is a fundamental breakthrough for the development of this direction.
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is fine to include aspirational goals as motivation as long is clear that these goals are not attained by the paper.
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Answer: [Yes]
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See Appendix J
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While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, worse outcome might be that reviewers discover limitations that aren't 't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an impor- tant role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed not penalize honesty concerning limitations.
3. Theory assumptions and proofs
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Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
Answer: [Yes]
Justification: We prove in detail in Appendix that GraphMaster can synthesize TAG data with consistent semantics and structure, and propose in detail the theoretical theorem and corresponding proofs on interpretable experiments in Appendix
Guidelines:
The answer NA means that the paper does not include theoretical results. All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.
All assumptions should be clearly stated or referenced the statement of any theorems.
The proofs can either appear in the main paper or the supplemental material, but they appear in the supplemental material, the authors are encouraged to provide short proof sketch provide intuition.
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Question:
Answer: [Yes]
We have described detail the principles of our model GraphMaster, as well as the experimental parameters, hardware configuration, and specific experimental methods. the same time, we also provide the specific creation algorithm for the data-limited dataset and the specific principles, parameter configuration, and hardware configuration of two self-created L.L.-based baselines. We believe that we have filled in all the details and promise to open source all the codes and datasets after the paper is accepted.
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(d)
The authors should provide scripts to reproduce all experimental results for the new reproducible, they We recognize that reproducibility may be tricky in some cases, in which case authors are welcome describe the particular way they provide for reproducibility. In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but it should be possible for other researchers to have some path to reproducing or verifying the results.
5. Open access to data and code
Question: Does the paper provide open access to the data and code, with sufficient instruc- tions faithfully reproduce the main experimental results, as described in supplemental material?
Answer: [Yes]
Justification: After the paper is accepted, we will open source the complete data-limited dataset and its creation code, the self-created code for the LLM--bsee baseline, and the detailed code of GraphMaster. We have submitted the GraphMaster code at this stage, but we believe it can still be improved. We will further optimize subsequent versions to ensure that it is plug-and- song-aad--payy playppay and has clear structure. You can find the code in Appendix ??.
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Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
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Answer: [Yes]
Justification: We included error bars in both the main experiment and the ablation experiment as mentioned in the main text. To ensure the robustness of our experiments, we repeated each experiment 50 times and reported the mean and standard deviation of the results. At the same time, for the interpretability analysis, we also performed multiple experiments (selecting multiple reviewers for averaging) to ensure the correctness of the experimental results.
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Question: Does the research conducted in the paper conform, in every respect, with the NeurlPS Code of Ethics
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Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer: [Yes]
Justification: We discussed the possible impact of our work on the community because our work groundbreaking. proposes new benchmark datasets and evaluation methods, and provides two baselines, and most importantly, proposes sota method GraphMaster, which we believe will have a very positive impact on the community and society.
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22

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A Comparison of GraphMaster with Recent Representative Works
Recent advancements in leveraging Large Language Models (LLMs) for graph-related tasks have produced several notable approaches. In this section, we conduct rigorous comparison between our proposed GraphMaster framework and two recent representative works: GAG [21] and LLM4NG (51). We structure our analysis across three key dimensions: motivation, methodology, and application scenarios, clearly delineate the unique contributions and advantages of Maste.
A.1
Comparison Between GraphMaster and GAG
A.1.1 Motivation
GraphMaster addresses a fundamental bottleneck in developing Graph Foundation Models (GFMs): the scarcity of large-scale, semantically rich graph datasets. Our work specifically targets the generation of high-quality text-attributed graphs in data-limited environments, focusing on both semantic coherence and structural integrity. In contrast, GAG [2I] primarily aims to simulate the dynamic evolution of social graphs through actor-item interactions, with emphasis on reproducing macroscopic network properties such as power-law degree distributions and small-world phenomena. While GAG attempts to capture emergent properties of large-scale social networks, it lacks explicit mechanisms for maintaining semantic coherence in node attributes, which critical requirement for training effective GFMs.
A.1.2 Methodology
GraphMaster implements hierarchical multi-agent framework formalized through the RAG paradigm, where specialized agents perform distinct functions within a closed-loop optimization system:
G = V RAG (a, G, Q, R, Arstrieve: Agenerate: Aevaluate)
(16)
where G is the original graph, Q represents the query formulation, I denotes the retrieval strategy, and Aretrieve: Agenerate, and Aevalvate correspond to the agent-specific functions. Our Perception Agent extracts knowledge through aamantic-enricher modularity maximization:
Q sem
1 2m
Σ 1,j
Ag 7
kggi
(1 7) -1--)) Ei,m doem (5it, Fm
dyem Asem((Irr (Fi, I.g))
8(c,,)))
(17)
2m
In contrast, GAG employs a bipartite graph model where homogeneous "actor" agents interact with items through retrieval system. Their S-RAG algorithm models actor-item interactions but lacks explicit quality control:
Poj = (0. sim( 2.2) (
(V. (N;)) + 0g
k2 max, K
+ 02
(18)
[N().)]
GraphMaster's 's multi agent architecture provides several key advantages: (1) Our Manager Agent dynamically optimizes a multi-objective utility function that balances semantic coherence, structural integrity, and class balance; (2) Our Evaluation Agent implements a comprehensive verification mechanism with adaptive thresholds; and (3) Our theoretical framework provides formal guarantees for information preservation, generation quality, and convergence properties.
A.1.3 Application Scenarios
GraphMaster is explicitly designed for enhancing text-attributed graphs in data-limited environments, making it particularly suitable for academic, industrial, and web-scale applications where data acquisition is costly or restricted. Our framework produces semantically rich and structurally coherent graph extensions that serve as high-quality training data for GFMs. GAG primarily focuses on social network simulation, with applications modeling online user interactions and social dynamics. While GAG can generate large-scale graphs (up to 100,000 nodes). it sacrifices semantic richness for scale and does not provide guarantees on the quality of textual attributes. Our evaluation framework, combining human assessment with Grassmannian manifold-based analysis, demonstrates that Gaspe consistently produces higher-quality graph extensions that better preserve both semantic and structural characteristics of the original data.
23

---

A.2
Comparison Between GraphMaster and LLM4NG
A.2.1 Motivation
GraphMaster addresses the broad challenge of data scarcity for GFMs through comprehensive graph synthesis that enhances both node attributes and structural properties. In contrast, LLM4NG [51 specifically targets few-shot learning scenarios node classification, with a narrow focus on enhanc- ing class-level information. While LLM4NG aims to improve classification performance by adding labeled examples, it does not address the fundamental issue of enhancing the overall quality and representational capacity of graph data. GraphMaster'ss approach is more comprehensive, as it treats the graph synthesis problem holistically, generating high-quality nodes and edges that maintain both semantic and structural coherence.
A.2.2 Methodology
GraphMaster employs a sophisticated multi-agent system with specialized roles and iterative refine- ment cycles. Our Enhancement Agent generates new nodes through conditional autoregressive model:
Pk II P(r))=5) Xx, Ex, K) 1=1
(19)
and models edge connections with probability function that balances semantic. structural, and degree-based factors:
P((V,, v.) E E((K) = -o -0(0) sim(x,, 21) + 02 [N(es) NK(u))] + 03 [Nx(c.)]
h;
(20)
max, ky
LLM4NG adopts a significantly simpler approach, primarily generating text based label char- acteristics alone. Its core idea is to let the large language model generate text that meets class characteristics based solely on the name of the label:
Sg LLM((Propp(()) C
(21)
followed by basic edge predictor that often introduces noise and structural inconsistencies:
ge(ho Ke,) MLP (he,)
(22)
Mrsth'' methodology offers several crucial advantages: (1) Our approach maintains structural
consistency through explicit modeling of node-edge relationships; (2) Our iterative refinement
process ensures high-quality synthesis through adaptive thresholds; and (3) Our theoretical framework
provides formal guarantees the quality and convergence of the synthesis process. LLM4NG lacks these quality assurance mechanisms and theoretical foundations.
A.2.3 Application Scenarios
GraphMaster addresses wider range of data-limite scenarios and enhance graphs for various downstream tasks beyond classification. Our framework is particularly effective scenarios requiring high semantic coherence and structural fidelity, such as scientific discovery, knowledge graph completion, and recommendation systems.
LLM4NG is narrowly optimized for classification tasks in few-shot scenarios, with limited impact on the overall graph structure. When applied to data-limited scenarios rather than strictly few-shot learning, LLM4NG's is edge generation methods often introduce significant noise and interference to the graph structure. This limitation is evidenced by its poor performance in our experimental evaluations on data-limited datasets. The edge probabilities predicted by its simple model fail to capture the complex structural patterns present in real-world graphs.
While LLM4NG offers computational efficiency through its lightweight design, it sacrifices synthesis quality, broader applicability, and introduces potential structural inconsistencies. Our experimental results demonstrate that GraphMaster achieves superior performance across multiple datasets and tasks, particularly in generating semantically coherent and structurally valid graph extensions that maintain both local and global properties of the original graph.

---

B
The implement details of newly created baseline
B.1
Mixed-LLM
Mixed-LLM introduces novel interpolative synthesis approach that extends the seminal mixup concept from computer vision to texl-attributed graphs via large language models. This method operates the principle of manifold-aware semantic interpolation, where node representations from nodes.
different classes are strategically combined to generate semantically coherent yet diverse synthetic
The Mixed- LLM algorithm consists of three primary phases:
Strategic Class-Balanced Sampling: Rather than random selection, Mixed-I.LMM employs a distribution-aware sampling strategy:
S {(u, si)lu P(e,) x 1/[V, [*)
(23)
where a is adaptive parameter controlling the emphasis minority classes and VV, represents the cardinality of nodes with label yt-
2. Latent Space Interpolation: For each pair of sampled nodes (us, Uj), Mixed-LLM generates convex combination in the semantic space:
3 X (LL + (1 -)- (L)M(
(24)
where ΦLLM represents the LLM's latent representation function and Beta(a,, a) is mixing coefficient.
3. LLM-Guided Textual Manifestation: The interpolated representation is transformed into coherent textual attributes through a prompt-based generation:
I3 LLM m 24, 2i, Me. W;))
(25)
where p a carefully designed prompt template instructing the L.M to create textual attributes that preserve the semantic characteristies of both source nodes while maintaining linguistic naturalness.
The final label assignment follows a soft probability distribution:
P(ys x Tly = c) - A) Ty c
(26)
where / is the indicator function. This probabilistic formulation enables Mixed-LLM to generate boundary-enhancing examples that improve classifier robustness.
B.2 Synthesis-LLM
Synthesis-LLM implements context-aware graph sampling and generative synthesis framework that leverages structural locality principles to inform LLM-based node generation. Unlike conventional approaches that process graph data indiscriminately. Synthesis-LLM employs sophisticated topologi- cal sampling to create representative subgraph contexts that maximize information density within LLM token constraints.
The framework operates in four sequential stages:
1. Multi-strategy Subgraph Sampling: Synthesis-L.LM employs hybrid sampling approach that combines Personalized PageRank (PPR) with strategic breadth-first search:
K, [PpR(G, u. r) U BFB d..d)
(27)
where PrrR samples nodes based their PPR scores from seed node v, with damping factor a and threshold r. while TEES complements this with depth- limite breadth-fisst expansion to depth d.
2. Structural-Semantie Context Formulation: The sampled subgraph is transformed into rich prompt context:
C Scontennt Ks, A[K,), X(K.))
(28)
where fcontert is a specialized function that encodes both topological relationships and textual attributes into structured prompt format.
25

---

3. Guided Generative Synthesis: The LLM generates new nodes conditioned on the extracted context:
(Ies E.) = LLMs(C,,)
(29)
where LLMs represents the synthesis LLM with temperature parameter 0 that balances creativity and fidelity.
4. Structural Consistency Enforvement: Generated nodes undergo topological validation to ensure adherence the original graph 's structural patterns:
E = fe E E (e))) > r}
(30)
where P Pstructure estimates the probability of edge existing given the structural patterns in G, and an acceptance threshold.
This methodology enables Synthesis- LLM to generate nodes that maintain both semantic relevance and structural coherence with respect to the original graph, while requiring minimal examples due to the LLM's inherent understanding of semantic relationships.
B.3 Experimental details
We selected QwQ-32B (37 as the large language model for these two baselines, and used two A6000 GPUs with 48G memory for the experiments.
B.3.1 Hyperparameter Selection for Mixed-LLM
In Mixed-LLM, extensive grid search and ablation studies were conducted to optimize key hyperpa- rameters. The class balancing parameter a was tuned within the range [0.5, 1.5] with an optimal value of 0.8, ensuring good balance between preserving the original class distribution and addressing class imbalance issues. The beta distribution parameter for the mixing coefficient, where À ~ Beta (a, a). achieved optimal performance at 0.4, producing meaningful boundary examples. Additionally, an LLM temperature of 0.7 provided the best balance between creative variation and semantic consis- tency, and incorporating 2-3 example interpolations in the prompt significantly enhanced generation quality.
B.3.2 Hyperparameter Selection for Synthesis-LILM
For Synthesis-LLM, the hyperparameters were optimized to capture both local and global graph structures. A PPR damping factor of 0.65 offered a suitable trade-off between local neighborhood exploration and distant node influence, while a PPR threshold nodes. A BFS depth limit of d = was sufficient to extract essential struetural context without overloading the LLM's input. Moreover, setting the generation temperature and semantic coherence, and a structural acceptance threshold of 0.6 successfully filtered edge proposals. Overall, the optimal performance was achieved when 25-35 representative nodes were included in the LLM context.
0.005 effectively identified relevant
0.5 ensured structural
C Data-limited Datasets Creation
To simulate realistic scenarios where annotated data is scarce, we generate data-limited datasets by extracting carefully curated subgraphs from the original large-scale graphs. Our procedure begins by partitioning the original graph based on node labels and inherent manifold properties, ensuring that the semantic distribution and community structures are preserved. Next, we apply multi-objective sampling strategy that leverages node degrees, community representation, and bridge node potentials to select a subset of nodes and their associated edges. This approach, outlined in Algorithm [ is designed to maintain the essential connectivity patterns and spectral features of the full graph while sigmificantly reducing the number of nodes. Iterative refinements are then performed to balance class proportions and correct any topological distortions, resulting in a smaller yet representative subgraph that closely mimics the original graph's 's structure and attribute distribution. This data-limited setup provides robust testhed for evaluating the effectiveness of our graph synthesis methods under constrained conditions.
26

---

Algorithm M-Preserving Graph Sampling
Require: Graph G = (V, E, X, M), sampling ratio E (0, 1]. convergence threshold
Ensure: Homeomorphic sampled graph Gs preserving manifold properties
1: 4 < -TG))
D Extract property tensor capturing distributions and spectral features > Optimize modularity for community detection
2: C f arg maxe Q(C', G)
3: D <- (Vy. where V yym {o V. V(o) M(() m) Create attribute partitions
4: 11 +- (Tym = -IV
Vymll/VV V/V
>> Joint distribution tensor Target counts per partition > Correct sampling counts to exactly match target size Initialize sampled node set
5: K
IV
6: K + K K+ -(K
7: V, -
8: for (y. E
do
9:
if V y. I K y.m then
10:
V. - V. y.m
11:
else
12:
S2 4g + Multi-objective weight vector where for each E Vy.m deg(v)
())m(() = A1 max, deg(u) + A2 [V,C(())
+ As,
(31)
[C(v)]
13:
V, + V. Topk y.m. Syym: K kym)
end if
15: end for
G² <- G
Initial induced subgraph
17: if [[((''. k(G)]l > c then
Check connectivity distortion
18:
B + Bridge nodes (V \ V.) sorted by connectivity gain potential
19:
R + Replaceable nodes in V. with minimal structural impact
20:
while [|m((')) - x[G)]l > and of and R + 0
21:
(b* r- f- arg ma S(b,r) subject to (bb) )((r) A M(B) = MMr)
22:
V. + (V, (
23:
G < G[V.]
24:
Update B, R
25:
end while
26: end if
27:
return G
D Statistics of the Datasets
Table 3: Dataset Statistics
Dataset
Nodes
Edles
# Classes
Louvain communities
# Training nodes
Validation nodes
Test nodes
Cora
2708
5278
106
1624
542
542
Citeseer
1911
Wikics
8196
104161
540
580
1769
5847
History
41551
251590
2036
24921
8337
8293
Arxiv2023
46198
38863
28901
28905
27718
9240
Children
76875
1162522
2296
46010
15455
15410
SubCora
1354
2486
815
267
272
SubCiteseer
1274
1360
486
764
255
255
SubWikies
1639
26786
111
350
1178
SubHistory
2077
40415
12
17
1249
416
412
SubArxiv2023
4174
SubChildren
3843
94636
24
71
2308
766
769
Table 3 shows the basic characteristics of our new synthesized dataset. We introduce our dataset from seven aspects: Nodes, Edges, Classes, Louvain communities, Training nodes, Validation nodes and Test nodes, including the original dataset and our newly generated Data-limited dataset.
27

---

E Hyperparameter Selection Analysis
We conducted comprehensive grid search experiments to determine optimal hyperparameter settings for GraphMaster. Our analysis reveals that the framework is robust to moderate parameter variations, with the following configuration yielding consistently strong performance across datasets:
Knowledge extraction: Sample size N 30 nodes provides sufficient context without introducing noise
Node generation: Setting M% - 15% of knowledge nodes balances quantity and quality
Community detection: Parameters μa = 0.5 and y = 0.5 effectively balance semantic and structural factors
Stochastic sampling: / 2.0 maintains appropriate exploration-exxpoitation balance
Edge formation: For semantic mode, (01, 02 03) (0.6, 0.3, 0.1); for topological mode, (0.2, 0.5, 0.3)
Quality assessment: Initial threshold To 7.0 with adaptive update rate 0.1
Convergence criteria: 0.05 provides sufficient refinement iterations
Objective weights: Initialize Asem
Astret
Abal 0.33 with learning rate 1] = 0.05
Among tested LLMs (QwQ-32B, Qwen-32B, PrepSeek-R13221 and LLaMA-33B) QwQ-32B
consistently delivered superior performance. We limited synthesis to maximum of 15 iterations, as additional iterations yielded diminishing returns..
F
Detailed Experimental Results
Table 4: Comparison of GraphMaster with other TAG synthesis methods in JKNet model.
Type
Mudel
Acc
Con
Fl
Adc
Choser
F1
Ace
Willes
Fi
Ace
Hisrey
F1
Age
Anar2033
F1
Aue
Chilkes
FI
Orignal
88981.1
87.94007
7833609
75
24.20033
43 13
76.344
$49.1.2
52.5m05
319969
GAIgO
7
MO0SHUT
53.0612 1.2
51,8-01
33.6114
1LM-Aug
LLMARGNN
Gopelai
91 4104
38.8 85..
6110
$10814
S14107
76.7:207
834-08
$1.400
88920..
K76130
48, 1.
7863611.2
760400
$9.1214
5138111
35.7=0.7
511-13
36.761.1
GripiSnnte
NA71504
4
7623144
743::11
78 3500
54 519150
453961
76238101
35.5113
3325505
G-Micap
878610
S7.0405
7644003
26:-10
767-10
766512
56:5-08
530-111
330-0.3
Clasio
GaphAdayn
77.5114
57.08133
33011122
RG-MMUTE
88M106
47.640
766407
347-004
8100:14
7005131
13010.9
440403
7644408
59.8:017
533::13
333-69
AGMicup
84710.1
36.113
316112
73.5-1
763-112
766104
K18169
42.9114
768117
$13-11
636-11
126110
GAG
910114
39.31111
$1.1077
300100
849.11
13211
18913
40K113
16911A
W:411
36.210
S0113
1LM
Moval-1M
LLMING
79.7
75.0 20.
74.5
M42
10.4
40.
370-008
89811.1
39.110.5
$45112
843-08
832114
4941
554108
$1.011
6:3:11
000.1.
40.11:4
GraphMaster
939112
936108
39.6113
819:00
864113
925187
6611111
$7.810
664:14
1.4
4.71
Table 5: Comparison of GraphMaster with other TAG synthesis methods in GraphSage model.
Type
Model
Childes
Arc
Ace
Aoe
Original
Origi
747-07
45.0 65.11
763498
517:0M
575-12
Classi Aug
GAng(
K79110
$1.910.
70LL13
76.000
K3710.
45.106
16K114
$5.1.15
$34.0K
9.M11.0
LLM-Au
LLMERGNN
OrgiiEai
010112
37.5614
79540.5
150189
333:00
834-099
0056607
50.1.5
87060.
47.6613
77961.1
563-005
15.510
354:14
36.1:0
337669
S66112
G-Mipp
K77100
$7.811
78210.77
75.3.1.1
709.112
77.51.04
44.211.1
7641077
$49.088
$3.6.122
33.31009
Clask: Sya
Iasaii
$111005
$1.5104
71.1111
7022:.
73.8:055
74.2113
82250.7
42.210.33
72:21100
53.3:0.3
4500009
31.75100
900-1.1
96.140.5
704409
7744-1
84.3-044
M7-11.
45. 45.4
315:03
$6.1-11
56.4-13
1511-05
FG-SMOTE
880106
$6,0110
708.004
76...07
90:3.13
772105
658109
4...11
716.06
$62.108
$43.111
S6104
AGMisup
67.7100
$7.310.3
81.51077
$3.5:0.4
31511.1
11M-Sya
LIMSNG
10.3
$1.2 104
10.
800 103
77.5
106
107
62.1
264 10.9
Misa--IM
91.4112
90.710.6
$4.110.9
847.1.
83.7104
81010.
W1210.7
58.111.1
$27110
39.604
62.3-13
4251:0
Sandaniss LLM
913300
90.24100
$433112
852:007
$48:009
12.150
90.550.5
90
S7.4214
$322103
53.5:12
63,4005
43.1209
GraphMMader
8.918.3
92.71111
SK91007
85.9 1U9
87.8 1.2
86.1 8.6
92.9
623111
37.4 103
66. 1.3
663
47.11
Table presents the experimental results using the GCN model, Table shows results using the JKNet model, Table displays results using the GraphSage model, and Table demonstrates results using the GAT model. Our findings indicate that GraphMaster consistently outperforms other approaches across all GNN architectures, strongly validating the universality of our proposed model. Furthermore,
28

---

Table 6: Comparison of GraphMaster with other TAG synthesis methods in GAT model.
Type
Clisser
HIasey
Acc
F1
Aec
Ace
ACC
F1
Aoe
Ace
P]
Orgenal
Orign
$5.040.
75.908
KUSH69
46 T
36.300
52.+10
55.0-07
Clask Aug
GAmgl
K361003
$6.710 70.
71.5 10
75.6 110
K2T1667
51.-11
GriplEa
7934009
25:4:11
29.35005
335510
2407
Gogelmmete
MR101
769112
$43:11
G-Micap
$48106
$4.3103
76.1088
73.2.1.2
73.5-0.7
75.4110
82.1..
43.410.5
760.109
$3...05
S1.:.12
51910.5
Clask Spa
Imshlic
786609
79.540 5405
09.40.5
04.3:15
71:5:119
72.1207
TMM60.5
41.3812
71.5407
515::18
43:9:0.7
30.7:0.8
Grehhrbhyn
M8800
3109
77.341.0
75.1-019
83.0-055
750-11
K37-1.3
44 3107
756-111
$47--1.2
$4.-110
S10-112
IG-SMOTE
AGMixup
630:007
33.710.
75.21.1
72.621.0
796:10.5
770100
41.610.
73:220
51.3:0.0
30.5:099
GAG
88.9-0.
37.0408
505400
74:3-0%
81:5:09
9001-0.1
M.100A
47.34009
77.14122
59-7-0.
96---67
358-066
11M--ya
MisalLLMM
LLMSIGG
776 10.3
682106
97.210.7
16.5 10.4
59.6 10.7
91.410.
67.9 10.
8:3:00
71.8 10.
81.9.07
16.6 10.5
7..21..1
R20 10A
67611.
41.7 10.7
$7.010.7
512 10.4
$0.1:05
$1.4 .0.7
$7.:110
46.9 004
60.7211
27.2 10.5
412:110
$7.04006
418::88
GraphMaader
we observe that our two novel baselines frequently achieve second-place rankings, which substantiates the significant potential of Large Language Models (LLMs)) in text-attributed graph data synthesis. As the first work leveraging LLMs for text-attributed graph synthesis, GraphMaster exhibits both efficient resource utilization and remarkable capabilities for TAG data generation.
G Detailed Graph Feature Analysis
Orignnl Croat Caph
Panepeech
Cuannng Naturn Pertuet 2313
Node
Node Degnes
Node Label
(a) Degree distribution
(b) Clustering coeffiecient
(c) Label homogeneity
Senevesone Grapp
Pramepomiity Smilerty
Cutanig DALT
Node Dogree
Node Label
(d) Degree distribution
(e) Clustering coeffiecient
(f) Label homogeneity
Figure 4: Graph feature analysis on Cora dataset. The top three rows of pictures are the results of the original data-limited dataset, and the bottom three rows are the results after TAG data synthesis using GraphMaster.
To rigorously evaluate the structural fidelity of our synthesized graphs, we conduct comprehensive feature analysis across three critical topological and semantic dimensions. Figure Figure Figure Figure 7 Figure [ and Figure present comparative analyses of the original graphs versus those synthesized by GraphMaster on Cora, Citeseer, Wikics, History, Arxiv2023, and Children datasets, respectively.
Each figure displays a comparative analysis between the original data-limited datasets (top row) and the GraphMaaseer-synthesizze datasets (bottom row) across three key metrics:
Degree Distribution (left column): Characterizes the probability distribution of node connectivity patterns. We employ the Kolmogorov-Sminne similarity, with lower KS statistics and higher p- p-values indicating stronger preservation of connectivity patterns. Our results demonstrate that GraphMaster consistently improves
(KS) test to quantify statistical
29

---

Origaal Grigh
Huropred aendrry tike
Sey!
25
Nots Label
(a) Degree distribution
(b) Clustering coeffiecient
(c) Label homogeneity
origae craps
loonogenblyy Smilarr: 3009
Node
Labil
(d) Degree distribution
(c) Clustering coeffiecient
Label homogeneity
Figure 5: Graph feature analysis on Citeseer dataset. The top three rows of pictures are the results of the original data-limited dataset, and the bottom three rows are the results after TAG data synthesis using Master.
Capph
Cueanig 2.30
Node
Node Degnes
Node Label
(a) Degree distribution
(b) Clustering coeffiecient
(c) Label homogeneity
Spatrelzee Biags
Frensprnhly Smilurly
Synhhstoed
ceeen ga1 2.7
Qurnng Patcr Oriiiiy 2717
Musio Deggnne
Nods Oogrne
Node Label
(d) Degree distribution
(e) Clustering coeffiecient
(f) Label homogeneity
Figure 6: Graph feature analysis on Wikies dataset. The top three rows of pictures are the results of the original data-limited dataset, and the bottom three rows are the results after TAG data synthesis using GraphMaster.
degree distribution similarity across all datasets, with Cora showing particularly strong results (KS statistic of 0.022, p-value=0.709).
Clustering Coefficient Degree (middle column): Reveals how local neighborhood connectivity varies across nodes of different degrees. We observe that GraphMaster substan- tially improves clustering pattern similarity in most datasets, with notable improvements in Wikics (from 0.728 to 0.777) and Children (from 0.785 to 0.835). This indicates that our synthesis approach effectively captures the relationship between node importance and community formation.
Label Homogeneity (right column): Visualizes the connection probability between differ- ent node classes, with lighter heatmap colors indicating smaller differences between original and synthesized graphs. GraphMaster achieves remarkably high label homogeneity simi-
30

---

O1g nat Creph
Syd Rice
Marigeealy So desiarty CURD
100
Node
(a) Degree distribution
(b) Clustering coeffiecient
(c) Label homogeneity
usi seaph
Spr disiadd Snat
est
Moregpmmity Se besiarlyy dima
144
Node Degree
Noce
(d) Degree distribution
(c) Clustering coeffiecient
(f) Label homogeneity
Figure 7: Graph feature analysis on History dataset. The top three rows of pictures are the results of the original data-limited dataset, and the bottom three rows are the results after TAG data synthesis using Master.
Croket Duggs
Weeceesiy Statidly Zire
14-
3044
Node Degrus
(a) Degree distribution
(b) Clustering coeffiecient
(c) Label homogeneity
onggat Graps
Grahh
Woreoganaty Sintarty coce
Mode Decrre
Node
Mode Label
(d) Degree distribution
(e) Clustering coeffiecient
(f) Label homogeneity
Figure 8: Graph feature analysis on Arxiv dataset. The top three rows of pictures are the results of the original data-limited dataset, and the bottom three rows are the results after TAG data synthesis using Master
larity scores (>0.96 across all datasets), demonstrating its ability to preserve label-to-label connectivity patterns.
Notably, our approach shows significant structural improvements on citation networks (Cora, Citeseer, Arxiv2023) where semantic relationships strongly influence topology. For instance, in Arxiv2023, GraphMaster improves degree distribution similarity from 0.930 to 0.950 while maintaining consistent label homogeneity (0.980). On larger and more complex datasets like Children, GraphMaster effectively preserves clustering coefficients (similarity improvement from 0.785 to 0.835) while maintaining degree distribution and label homogeneity.
These results collectively demonstrate that GraphMaster not only enhances semantic richness but also successfully preserves-ane and in many cases improves-the the critical structural characteristics of the
31

---

Crichal Grige
Syyte Syrtestoetogg
Parogred drrieery
BRO 11
12
Node
(a) Degree distribution
(b) Clustering coeffiecient
(c) Label homogeneity
Urgret Grapp
deciucity timea
Hears Dass
200
30
Node Decres
107
Node
(d) Degree distribution
(c) Clustering coeffiecient
Label homogeneity
Figure 9: Graph feature analysis on Children dataset. The top three rows of pictures are the results of the original data-limited dataset, and the bottom three rows are the results after TAG data synthesis using Mrster
original graphs. This structural fidelity is essential for ensuring that downstream GNN applications trained on synthesized data generalize effectively to real-world scenarios.
H Theoretical Analysis of Interpretable Experiments
Recent advances evaluation frameworks have emphasized the importance of interpretability when evaluating language model outputs after knowledge edits [23] and other fields [6]. Inspired by this, interpretability analysis similarly integrates both human-centered and algorithmic assessments to evaluate the semantic coherence of synthesized graph components.
H.1
Human-Centered Interpretability Evaluation
The human evaluation protocol assesses GraphMaster's 's interpretability through structured multi- dimensional analysis. We recruited R = 50 expert annotators with backgrounds in graph machine learning, natural language processing, or data mining. Inclusion criteria required either (i) at least two peer-reviewed publications in the past three years, or (ii) a minimum of two years of relevant industrial R&D experience. All participants with potential confficts of interest were excluded. The final cohort consisted of 38 PhD students, postdoctoral researchers or research assistants, 3 faculty members, and 3 industry researchers. Geographical distribution included Asia (50%), Europe (20%), North America (24%), and others (6%).
Each expert evaluated N = 200 synthesized instances sampled from six benchmark datasets. Each instance was independently scored by three reviewers, resulting in 600 total annotations. The evaluation covered three critical dimensions:
Process Transparency: Measures how clearly the synthesis workflow can be understood, from initial graph analysis to final node generation.
Decision Justification: Evaluates whether the model's choices (e.g., which nodes to sample, what attributes to generate) have clear rationales.
Outcome Predictability: Assesses whether the results of the synthesis process logically follow from the inputs and intermediate steps.
Fach dimension was rated using 1-5 Likert scale, which was linearly normalized to |0, 1] for scoring purposes. For each dimension d, reviewer r assigns a score ly,a,d E [0, 1] for instance The composite interpretability score for instance by reviewer is:
32

---

b
(32)
To ensure annotation quality, reviewers completed 30-minute training session followed by a 20- item calibration phase. Only those achieving a Cohen's k. 2 0.70 in the pilot study were retained. During the main annotation phase, adopted double-blind setup with randomized item and order presentation, applied a standardized rubric, and removed two raters who failed attention checks. The average pairwise inter-rater agreement achieved was 0.72 0.05, indicating moderate to substantial agreement.
To assess the statistical reliability of the aggregated Traceability Score, compute bootstrap confidence interval:
oj T 2a/21/ R. N
2 T Tecore 2a/2
TT R
CI
(33)
N
where of denotes the variance of individual instance-level ratings and 20/2 is the critical value associated with the desired confidence level.
We provide complete details of the reviewer selection, demographic statistics, annotation procedures, and inter-rater reliability metrics in the appendix.
H.2
Grassmannian Analysis of Semantic Consistency
H.2.1
Theoretical Foundation
Definition (Grassmann Manifold). The Grassmann manifold 9 d) is the set of all p-dimensional linear subspaces of p.d. Each point G(1, d) can be represented by a unit vector gd-1 (up to
sign).
Theorem (Principal Semantic Direction). Given a set of semantically related unit-normalized text
embeddings {x1, X2,..., ,,...,,,, X C Sd-1, there exists an optimal direction u² gd-1 that minimizes the sum of squared geodesic distances on the Grassmann manifold:
11" arg min
Σ arccos? (uuccoss(u"xx))) [[x'x))) 1=1
(34)
we2d
Proposition (Semantic Coherence Metric), For synthesized node with embedding x,, its semantic coherence with respect to background knowledge is quantified by:
S(x,) = arccos((x u²l) (xTu"l)
(35)
where S(x,) E [0.1] with higher values indicating greater semantic coherence.
Proof of Theorem I (Principal Semantic Direction). Let us first establish that the geodesic distance between two points on G(1, represented by unit vectors u and v given by dg (u, v) = arccos( lu" vl).
Consider the objective function f(u) ΣK arccos? (Iu" We need to show that:
1. This function has at least one global minimum on 8d-1
2. This minimum represents semantically meaningful direction
For the first point, note that f is continuous on the compact set $d-1, so by the extreme value theorem, it attains both maximum and minimum value.
33

---

For the second point, let us analyze the critical points of constrained to gd-1 Using the method of Lagrange multipliers, we seek critical points of:
C(u, X) =
arccos (occos2((uss (lu"x,) A(u" 1)
(36)
Taking the gradient with respect to u.
K
VuL Σ - Parccos(1u (/u" x;() v1 (lu"x,1²
sgn(u"xx, x2) xx),x, 2Au
(37)
Solving this system of equations equivalent to finding u that balances the weighted contributions of all x, vectors. The solution u" represents a direction that minimizes angular deviation from all input embeddings.
To demonstrate semantic meaningfulness, let us decompose any embedding X, as:
X; (xf u')f"")"" "ouuu"*x""" +x
(38)
where x is orthogonal to u'. The optimization objective minimizes the magnitude of these orthogo- nal components across embeddings, effectively capturing their common directional component in the embedding space. Since semantically related concepts tend to cluster directionally in embedding spaces, u² represents their central semantic direction.
Proof of Proposition (Semantic Coherence Metric). The geodesic distance between x, and the principal direction u' on G d) is (x u²) = arccos( u*)). This distance ranges from (perfect alignment) л x/2 (orthogonality).
To normalize this to similarity measure in [0, 1), we apply the transformation:
S(x,) 1- dg (x,, u²)
2
arccos( xx_ u")
(39)
π 7/2
This yields S(x,) = when x, and u are perfectly aligned (modulo sign), and S(x,) when they are orthogonal.
The semantic interpretation follows from the properties of the embedding space: cosine similarity a standard measure of semantic relatedness in text embeddings, and our formulation extends this concept the Grassmann manifold, providing geometrically principled approach measuring semantic coherence against a central concept.
Moreover, this metric satisfies several desirable properties:
Invariance to sign: 5(x.) 5(-x,), reflecting that oppositely directed vectors represent the same point on ((1,d)
Monotonicity: S(x,) increases as x, aligns more closely with u²
Bounded range: S(x,) E [0, 1]. facilitating interpretation
Geometric meaning: Directly related to the principal angle between subspaces the Grassmann manifold
Theorem (Computational Solution). The principal direction u² can be efficiently computed as the principal eigenvector of the matrix:
34

---

K M Σ
x
(40)
2-1
where weights wj are iteratively updated based on angular distances.
Proof. While the original optimization problem involves the non-linear arccos function,
develop an iteratively reweighted least squares approach that converges to the optimal solution.
Consider a simplified objective function g(u) = Σ wy( - (u" x,)²) where wy =
arccos( uu/ xz xx1l/v1--a (lu/ x,1)² iteration Expanding g(u):
K
((u)
Σ
wy
Σ
w/
(u"
x2)
(41)
1=1
j=1
K
k
Σ
Wy
T
Σ
w
(42)
j=l
/=1
K
Wi - u Mu
(43)
Since is constrained to have unit norm, minimizing (u)) is equivalent to maximizing u Mu. By the Rayleigh-Rinn Ritz theorem, this maximized when u is the eigenvector corresponding to the largest eigenvalue of M.
The algorithm proceeds as follows:
Initialize un as a random unit vector
2. At iteration
Compute weights wi = arccos (/cccos(/uxxxxx x,,Tx D/1
Form matrix M, ΣØ=1 wyx,x]
Update u++1 as the principal eigenvector of M,
3. Repeat until convergence
This approach is guarantced to converge a stationary point the original objective function. Since the objective function is well-behaved on gd-1, this stationary point corresponds to the desired minimizer u².
H.3
Statistical Analysis of Evaluation Results
The distribution of semantic coherence scores provides valuable insights into the model's ability to generate semantically consistent nodes. We analyze this distribution through statistical hypothesis testing, comparing the mean coherence score Š against null hypothesis of random semantic alignment (Hu S = 0.5). The one-sample t-test yields t-statistic:
5 0.5
f = 8/VM M
(44)
where s the sample standard deviation and M is the number of synthesized nodes. This allows us to quantify the statistical significance of semantic coherence in our synthesized graph elements.
35

---

Additionally, we compute the Pearson correlation coefficient between human interpretability ratings and semantic coherence scores across matching instances to assess the alignment between human judgment and our geometric approach:
5)
p V S)²
(45)
where is the average human rating for instance and S; is the corresponding semantic coher- ence score. This correlation provides evidence for the validity of our dual- persppettee evaluation framework.
I Theoretical Analysis of Agent Capabilities
In this appendix, present rigorous theoretical analyses validating the effectiveness, of GraphMas- ter's agent components. We first develop foundational mathematical framework for each agent's operations, then derive formal guarantees regarding their synthesis capabilities through series of interconnected theorems and propositions.
L1 Information- Thiormation-Thhorettee Analysis of the Perception Agent
We establish a theoretical framework for analyzing the information capture capabilities of the Perception Agent through the lens of information theory and spectral graph theory.
Definition 2 (Topological Information Density). For text attribute graph G (V, 8,,8,,0 (,, the topological information density I(G) is defined as:
I(G) [V] Σ H (pN(Ca)+ +
E KL(Pe, lpe) VyENT(.))
(46)
where H-) is the entropy function, PN((:)) is the probability distribution over the neighborhood of vs Po; the local distribution node Vj, and pg is the global distribution over the graph.
Theorem 3 (Information Capture Properties of the Perception Agent). Given text-attributed graph X.Y) Y), the Perception Agent constructs:
(V, V,
1. An environment report R2 that preserves topological information with bounded distortion:
log VI
DKL(I(9))((R)) S 5 +0
(47)
2. A knowledge encapsulation Krç that preserves semantic relationships with fideity::
- >6) < 2exp
(-2
2
P sup PLMREY
C2
(48)
where S(---) is semantic similarity function, K4) its empirical estimate based on Kr, and CS is problem- sepcfff- constant.
Proof. We decompose the proof into topological and semantic components.
Part 1: Topological Information Preservation
The environment report R, = (Pglobal, (P Jc Pecom i= Daructs Deom) encodes compressed representation of the graph's 's topological properties. We establish information-theorete bounds on this compression.
|C|
For the degree distribution captured in Damuet
C log |
Wz Pg
pR.y 3 deg! dog
(49)
36

---

where W22 is the 2- Wasserstein distance and C₁ is universal constant.
For spectral properties, the Laplacian spectrum satísfies:
(g)
Pi Pinax
log |
I C21
(50)
Amax (g)
[
where (9) is the i-th eigenvalue of the normalized Laplacian of 9, and Pj is the corresponding encoded value in Pglobal.
The community structure preservation follows from the Cheeger's inequality:
R₁ encodes hS,),) such that A2
S hg S (22
(51)
2
where (S,) is the Cheeger constant for community S.-
By aggregating these bounds and applying the data processing inequality, we establish the KL- divergence bound on topological information.
Part Semantic Preservation
The knowledge encapsulation ICt constructed via semantic-enriched modularity maximization and personalized PageRank sampling:
1 2m
ki,k
dsem(z;, Iz)) Et.m dsem (Il, Tmm)
sem
Aij
7
2m
(1 11-19 7)
o(cr,
(52)
For any semantic function S with Lipschitz constant Ls, the c-net covering number of the semantic space is bounded by:
NC,, X, drem) S (D))
D d
(53)
where the diameter of the semantic space and d is its dimension.
The Personalized PageRank algorithm ensures that sampled nodes provide a 5-cover of the semantic space with probability at least 5 when:
1 8
dlog ( log 1
|&
(54)
Applying the McDiarmid inequality to the empirical semantic distance estimates completes the proof of the high- probbbbbbbbblitt bound.
Corollary 1 (Spectral Approximation Guarantee). The knowledge encapsulation Kt provides (a, (..33)-spccttl approximation of the original graph with high probability, such that:
x" LGx S Lxxx S B x" LGx
Vx RRIV
(55)
where Lg and LK, are the respective graph Laplacians, and a, B depend on [Ke), the spectral gap of G. and the teleportation parameters of the PPR algorithm.
Proof. Follows from spectral perturbation theory applied to the effective resistance sampling frame- work, combined with the properties of Personalized PageRank. The core insight lies in the fact that PPR sampling approximates effective resistance sampling when the teleportation vector properly calibrated.
I.2 Cemannic-Topologiin Coherence of the Enhancement Agent
We now establish theoretical guarantees for the Enhancement Agent's generation capabilities, bridging statistical learning theory, concentration incqualities, and manifold theory.
37

---

Definition 3 (Semantic- Topoooiica Coherence). For generated node Vs with attributes 2, and connections g the cenantic-topologee coherence is defined as:
(1ss; () = a Crem (I.s X) + (1 -x). Cipp 9)
(56)
where Cuem is semantic coherence, Cropo is topological coherence, and [0,1] is weighting parameter.
Theorem 4 (Generation Consistency with Concentration Bounds). The Enhancement Agent produces node U with attributes 2, and edges & that satisfy:
1. Semantic consistency with concentration bounds:
|Kggtt2
P (|Cssem (1: X) sem X)] > 5) 2 exp
(57)
20 sem
2. Topologicul consistency with respect to local and global network properties:
ElCiopo 9)1 2 B max (E
(58)
eyEV,
[/3
3. The generative mechanism preserves manifold structure in the asymptotic limit:
lim IP (dist(x,.,xx) =)
(59)
Ke
where Mx the manifold on which the original node attributes lie.
Proof. Part 1: Semantic Consistency Concentration
The Enhancement Agent generates node attributes via conditional autoregressive model:
P(x, Ra) = II P Nr, R.)
(60)
Each token generation can be decomposed as:
((x1)"))
P( Nrx &x, R.) x exp
(61)
where h, is the contextualized representation and is a temperature parameter.
The contextualized representation integrates information from the knowledge encapsulation:
h; W, fxx55) W2 g(X(s) W; h(Ex))
(62)
Let Cuem (I,, X²) max ex S(I,,, 2yy). The semantic similarity function S satisfies the bounded differences condition:
[8( S(x,, Ls [x, -xyl
(63)
By applying McDiarmid's inequality to the empirical semantic coherence, we establish the concentra- tion bounds.
Part 2: Topological Consistency
The edge generation mechanism:
P((v., v.) E Ke, R.) = -01
sim(z,, (OI-sim(zs,,.) 02 [((x)) (Nc(vs)) (K((s))
k;
0g max, ky
(64)
We analyze the asymptotic properties using random graph theory. Let G(n, P(j) be a random graph model where edge (i,j) exists with probability Pij.
The expected clustering coefficient:
Ens Pis Pis Pij
E(CC(v,))
(65)
Eis Pis Pis
38

---

For our model:
Pij 0 (0. 100000000mmaaa sim(m;; m3) 02
(W(xs) (W((s))
hg
+ +03
(66)
[W(vs]
max by
The local-global consistency emerges from the balance between the three terms:
0 term preserves homophily (similar nodes connect)
02 term ensures triadic closure (friends of friends connect)
0g term maintains scale-free properties (preferential attachment)
Using graph limit theory, the topological consistency governed by:
()) x= WW&, Wgl D
(67)
where Wes and WG are graphons (limiting objects of graphs) and | la the cut norm.
Part 3: Manifold Preservation
Let Mx C d be compact r-dimensional manifold with condition number 1/7 and reach at least T >
The nodes = 11,-.. .,, z are sampled from distribution supported on Mx.
The language model learns conditional distribution:
(LIM(1(KA) Z(K,) exp (-
dist(x, (ixx()))((KK)))))
Pprior( (x)
(68)
202
As |CC|] co, we have:
Mx(K,) Mx in the Hausdorff metric
(69)
The manifold preservation follows from the consistency of kernel density estimators manifolds and the properties of autoregressive language models.
Proposition (Latent Variable Interpretation). equivalent to a controlled stochastic differential equation on the graph manifold:
The Enhancement Agent's generative process is
da. = pl K.) + o K.)dW,,
(70)
where y is drift term aligning generation with the knowledge encapsulation, a is a diffusion term controlling diversity, and W, Wiener process.
Proof. We establish the equivalence by showing that the discrete token generation process converges the SDE in the continuous limit. The proof uses techniques from the theory of diffusion models and score-matching generative models.
I.3 Theoretical Properties of Quality Assessment and Convergence
We now establish rigorous guarantees for the Evaluation Agent's quality assessment and convergence determination capabilities.
Definition 4 (Marginal Quality Contribution). For graph G (V,E, and a generated node vs with altributes vs and connections Ex, the marginal quality contribution is:
AQ 9) Q(G U (v., &,)) - Q
(71)
where Q a quality functional measuring overall graph utility for the target task.
Theorem (Quality Assessment with Statistical Guarantees). The Evaluation Agent implements quality assessment function that satisfies:
39

---

1. Strong correlation with marginal quality contribution:
1 [V
El 9))LLM (v,, Ro, Rr, Ki) - =
(72)
where is monotonically increasing and Lipschitz continuous.
2. Thresholding efficiency with probabilistic guarantees:
P(AQ(v.. 9) > 0|LLMv (vs, Ro, Rt,K,) > Tτ) 2 exp(-e (G]))
(73)
for some constant 0 and appropriately chosen threshold Tt.
3. Convergence detection with statistical significance:
PP P(Converged; = L|E[ < <c)
(74)
where decreases exponentially with the window size k in the convergence criterion.
Proof. Part 1: Correlation with Marginal Quality
The Evaluation Agent employs multi-dimensionn quality assessment:
LLMy Ro, Re, K.)) (mssom (Ua, K2),, PPopo(us Gr), Pia (v,, 34)
(75)
The quality functional Q can be decomposed into semantic, topological, and balance components:
(()) = wi Q Qem (9) + w2 Qupo upo (9) + wa (gQhi(
(76)
For each component, we establish bounds the approximation error:
C₁
IE (eQsem 9) seem (Ws, Kt) = =s] - -sse(()) S
(77)
KCt
C2
[E iooo 9) (Us, Gt) fcopo 5
(78)
V
C3
(U 9) Poal (v., V/t = bj fba((b) 5 v
(79)
By the properties of the LLM's function approximation capabilities, we have: w2 Swpo
lg w1 Ssem
W/ Sbal oo <11M
(80)
Combining these bounds establishes the correlation with marginal quality.
Part 2: Thresholding Efficiency
The adaptive threshold mechanism:
= T 7 ((F((w)) (w)-)=
(81)
converges to optimal threshold r* that maximizes expected improvement:
arg max E (AXXEx(((()))
(82)
where (T) V. ILLM (1s,
By the properties of sub-Gaussian random variables and the Lipschitz continuity of f. we establish the high-probability guarantee on positive quality contribution.
Part 3: Convergence Detection
The convergence criterion:
Converged, = 1l ( max [F((W])
Fe-,(wH-,)) < SEA LL Mggal (Ro, R.) = True
(83)
40

---

implements a Shange-point detection algorithm with multiple hypothesis testing. The probability of false convergence detection is bounded by:
2nE²
P(Converged, = [[I k sk
(-
(84)
C
where n is the number of samples used to estimate F& and Co is a bound on the quality range.
Likewise, the probability of missing convergence is bounded by:
ne²
P(Converged; = < S exp
(85)
802
The combined error probability decays exponentially with the sample size and window length.
Corollary 2 (Optimal Stopping Property), The Evaluation Agent's convergence criterion implements an approximately optimal stopping rule for the synthesis process, achieving regret bound of:
Regret(T) So (
(86)
where T is the maximum number of iterations.
Proof. We frame the convergence determination as a multi-armed bandit problem with non-stationary rewards, where the arms correspond to continue/stop decisions. Applying results from optimal stopping theory and the regret analysis of UCB algorithms yields the result.
1.4 Unified Multi-Agent System Dynamics
We now establish theoretical results concerning the collective behavior of the agent system, framing it optimization Riemannian manifold.
Definition 5 (Graph Configuration Manifold). The space of possible graph configurations forms Riemannian manifold MG with metric tensor:
9h (C) = Ollg (() log ((()))
(87)
00:
08;
where p(G(0) a parametric model of graph distribution and are the parameters.
Theorem (Manifold Optimization Dynamics). The multi-agent system in GraphMaster implements
natural gradient ascent the graph configuration manifold MG, optimizing mulli-objective function:
(g) = A Vsem(g) + >2 streer( ( + Ag (9)
(88)
with adaptive weights X; that evolve according
x = 11a [M+ P
(89)
where ITA is projection onto the probability simplex. The convergence rate is:
1 VV 50 for a non-convex objective, and:
(90)
/T
log T
(91)
the objective satisfies Polyak-Lojasiewicz condittonn.
41

---

Proof. Part 1: Natural Gradient Dynamics
At each iteration, the system performs:
Gt+ = 11Mg [G, + 7h (G.) (V))
(92)
where I(G;) is the Fisher information matrix and MLMg is projection onto the manifold.
This update is implemented by the collaborative agent process:
A; = (GG))
(93)
D L Rr, M,)
(94)
E ((v,. P((ss) vz) Ke, Rr) m11}
(95)
V accepted
= {ur E V LLMV (Us, Ro, Re, Ke)) > Tt}
(96)
G Gr (Vicceptedss Ed1 Viceous)
(97)
Part 2: Adaptive Weight Dynamics
The Manager Agent implements online gradient- based multi-objectiive optimization:
lI | VVA,
(98)
where P(G:) measures progress toward synthesis objectives. This follows the Multiple Gradient Descent Algorithm (MGDA) for Pareto optimization:
V (G,) V (Go)
V P(Gt) Vx, min
(99)
V (9))
Part 3: Convergence Analysis
For general non-convex objective, we have:
(G)))))) 2g (G
+ nL
E
min
(100)
rf
2
For appropriately chosen step size 7 (1/vTT). this yields the O(1/VT) convergence rate. the objective satisfies the Polyak-t.ojasievicz, con.itttiiiiiiiiiiiiiiino
condition:
(V 21"1"" 4 (G)]
(101)
for some μ 0, then we obtain the improved O Tog rate.
Theorem 7 (Spectral Convergence of Synthesized Graphs). Let Gorig be the original graph and G be the synthesized graph after convergence. Under appropriate conditions on the synthesis process, the eigenvalue distributions of their normalized Laplacians converge:
lim dip(Pgmm Pg.m) = |V|+00
(102)
where dip is the Lévy-Prokhoros metric between spectral distributions and Pg the empirical spectral distribution of graph G.
Proof. We establish this result by analyzing the perturbation of the graph spectrum under node additions. The proof relies on matrix concentration inequalities and recent results in random matrix theory concerning deformed Wigner matrices.
For graphs with bounded degree, the spectral distribution satisfies semicircle law in the limit. The synthesis process preserves this property through controlled edge formation that maintains degree distribution and local clustering patterns.
42

---

These theoretical results collectively establish the mathematical foundations underlying GraphMas- ter's effectiveness, providing formal guarantees for its information preservation, generation quality, evaluation accuracy, and convergence properties. The integration of concepts from information theory, statistical learning, manifold optimization, and spectral graph theory creates a comprehensive theoretical framework that explains why our multi-agent approach succeeds in generating high-quality text-attributed graphs in data-limited environments.
J Limitations and Future Work
While GraphMaster demonstrates significant advances in text-attributed graph synthesis, several important limitations remain. The current background knowledge selection mechanism, while effec- tive for moderate-sized graphs, faces challenges with very large heterogeneous graph structures. Specifically, our Personalized PageRank-based approach may prioritize structurally central nodes while potentially undersampling semantically important but topologically peripheral communities. This sampling bias can result in synthesis blind spots, particularly in graphs with highly skewed degree distributions or multiple disconnected components with distinct semantic characteristics. Additionally, the quality evaluation process relies on LLM capabilities that exhibit variance across dif- ferent base models and parameter scales. This interdependence creates challenges for deployment in resource-conee cooutre-constrained environments where smaller models are preferable. Computational requirements present another significant limitation. The multi-agent architecture, while theoretically elegant, incurs substantial inference overhead as each agent performs multiple LLM calls per synthesis round. For extremely large graphs, the current approach requires non-trivial batching strategies and optimization techniques that may not be readily available in standard deployment scenarios.
For future work, we envision several promising research directions. First, hierarchical multi- resolution synthesis could revolutionize the approach by simultancously modeling graph characteris- tics at multiple levels of abstraction from global topological patterns to local semantic neighborhoods. This layered perspective would enable more coherent synthesis that preserves both macro-structure and miero-semantics while potentially reducing computational complexity through selective refine- ment. Second, cross-domain knowledge transfer represents frontier challenge, where synthesis agents could leverage patterns learned from data-rich domains to enhance generation in data-scarce domains without explicit domain adaptation. This would require fundamental advances in domain- agnostic graph representations that capture universal structural and semantic patterns transferable across vastly different graph types. Rather than relying on fixed sampling algorithms, the frame- work could develop adaptive sampling policies optimized specifically for the synthesis objective. Finally, exploring emergent graph properties from synthesis could yield insights into how lo- cally generated elements collectively produce global graph characteristics that were never explicitly revealing new theoretical connections between local generation rules and emer- gent network phenomena. These developments would not only address current limitations but could fundamentally reshape our understanding of generative modeling for complex relational data structures.
K
Case Study
In this study, we conducted case study track the entire synthesis framework and documented the complete enhancement process. Table presents the background knowledge possessed by our Perception Agent prior to generating the environmental perception report, which serves as a critical basis for the environmental status report generated in Table [ In Table 8 not only the environmental status report produced, but the Manager Agent's decision for the current round-namely, semantic enhancement-is also provided. Subsequently, based on the information from the semantic enhancement, the Perception Agent conducted a sampling of the background knowledge nodes, as illustrated in Table (fr (for brevity, only two nodes are displayed). Following this, Table preseens the node information generated by our Enhancement Agent (owing to space limitations, the abstracts of some nodes have been omitted). Finally, Table I1 exhibits the quality evaluation of the generated nodes by our Evaluation Agent. As indicated in the table, the newly generated "new_now _ww_ndde33 5" was omitted due to substandard quality, and the table further demonstrates that the Evaluation Agent judges the entire synthesis process as not yet converged, necessitating continuation to the next round of enhancement.
presents
43

---

Table 7: Semantic Enhancement Case Study
Dataset: <SubCora>>>
Initial text attribute graph features:
"Graph": ""num_mn :odm__ 1354,
"num_ @num_edges": 2486,
"avg dovg_degree". 3.672082717872969,
"density": 0.w.00.000100000 4003004551064
"clustering_ceeffccien"" coefficient":
"avg_path lenghh" 6.52047030925269,
coonectted compoleentttmmmm 78,
Boargstt_compnnie size": 1223,
"indices": [4, 2, 18, 11, 6, 15, 12, 9, 16, 10, 5, 13,
17, 8, 20, 21, 24,
22,
23,
19, 36, 29,
27, 33, 47, 79, 25, 26, 30, 31, 34, 35, 37, 38, 40, 45, 50, 52, 53, 55, 56, 59, 62,
64, 66,
67, 73,
77, 80, 83, 28, 32, 39, 41, 42, 43, 44, 46, 48, 49. 54, 58, 60, 61. 63, 65, 68, 69, 70, 71,
72, 74, 75, 76, 78, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 14).
"sizes": [192, 106, 97, 84, 78, 74, 65, 56, 55, 46, 37, 33, 18. 15, 14,
10,
1, 1].
("4": 14,11:11112220100101000011111111
"21": 14, "24":
".9": 3."5"::
"50":
2800":
"48": 1,
",""""
"85": "86": 187"" 1,
1.
",
"96": 1, "97": "98":
1,"",,
"statistics":
"4":
"size": 192,
"internal "internal_edges":44 423,
"fractionnoof_grapp" 0. 0.14180206794432422.. "noduulartv_conttribtton" 0. 0.103009273492297
J.
"2":
"size": 106,
"internal iinternaleedges::: 156,
"fraction of graph": 0.078286558335542233.
imodularity_ccnnribbtie 0.0527271303366666
).
) "SStructraaDisstrbbutoo""" "degree_ ("4": 233, "14": 4,"7" 33,"3": 273."5":
127."31": I....
"SemmaticDistriuuttoo"": "placeholder": "Semantic distribution analysis would go here" "LaaelDistributioon:: "22": 410, "4": 110, "6": 175, "1": 208, "3": 214, "5": 90, "0": 147 }. "ClassStatistics": [ ["2": "count": 410, "fraction": 000280649926147544 "internal edgess" 518, anvg_degred 2.526829268292683, "community ddistibbutiiiiii { "18": 26, "2": 20.)
}
44

---

Table 8: Semantic Enhancement Case Study
Dataset: <SubCora>>>>>
Initial Environment Report:
Summary:
The graph under analysis contains 1,354 nodes and 2,486 edges, resulting in sparse but structured topology with an average degree of 3.67 and an overall graph density of 0.0027. typical in real-world networks, where the number of edges scales sub-quadratically with the number nodes. The average shortest path length of 6.52 indicates that the network is relatively well-connected in its largest component, despite the overall sparsity. The clustering coefficient, a measure of local cohesíveness, is 0.23, suggesting that nodes tend form tight-knit neighborhoods-ann important property for tasks that rely on local structural features, such as label propagation and GNN-based message passing. Structurally, the graph is partitioned into 78 connected components, but worth noting that the largest connected component comprises 1,223 nodes, accounting for over 90% of the entire graph. This makes the graph effectively connected for most practical purposes, enabling global information diffusion across the majority of nodes. Community detection reveals a complex and heterogeneous modular organization. There are several large communities, such as those of size 200, 112, 97, and 96, indicating strong intra- cormanitmuntty connectivity core regions. These large communities contribute significantly to the overall modularity, with notable examples such as community "4" (size 200, modularity contribution so O. 1052) and "3" (size 112, contribution 0.0631). However, the long tail in community size distribution is striking:
Such characteristics
1)0ver 40 communitics consist of only 1 or 2 nodes, 2)More than 60% of communities are smaller than size 10.
This imbalance in community sizes suggests that while the graph's topology supports clear structural groupings in dense areas, it also contains many fragmented peripheral regions that are less well-served by structural clustering alone. These small communities may represent semantic outliers, conceptually related nodes that not densely connected in the topology. Overall, the graph structure is well-organized, with a solid backbone of connectivity and modular clusters. The community detection result aligns with expectations in real-world data but also reveals opportunities for further refinement-parriculaary in regions where structure alone fails to form meaningful communities.
Given this context, incorporating semantic node features (c.g.. textual content, embeddings, or metadata) could greatly complement the topological view. Nodes in small or singleton communities may be structurally isolated but semantically similar, and semantic enhancement can help bridge this gap, improving community coherence, label consistency, and downstream task performance such as classification, recommendation, and contrastive learning.
Manager Agent's Decision: (Semantic Enhancement)
45

---

Table 9: Semantic Enhancement Case Study
Dataset: <SubCora>>
Perception Agent Knowledge Extraction:
Sab {
"node_ip mnode_id": 536.
"label": 0,
"text": "Title: Dynamic Constraint Satisfaction using Case-Based Reasoning Techniques \n Abstract: The Dynamic Constraint Satisfaction Problem (DCSP) formalism has been gaining attention as valuable and often necessary extension of the static CSP framework. Dynamic Constraint Satisfaction enables CSP techniques to applied more extensively, since it can be applied in domains where the set of constraints and variables involved in the problem evolves with time. At the same time, the Case-Based Reasoning (CBR) community has been working on techniques by which to reuse existing solutions when solving new problems. We have observed that dynamic constraint satisfaction matches very closely the case-based reasoning process of case adaptation. These observations emerged from our previous work on combining CBR and CSP to achieve constraint-basee adaptation. This paper summarizes previous results, describes the similarity of the challenges facing both DCSP and case adaptation, and shows how CSP and CBR can together begin to address these chal lenges.". "neighbors":
639
"mask": "Train"
"node id": 41,
"label": 0,
"text": "Title: A Memory Model for Case Retrieval by Activation Passing \n Abstract: We present a tree-structured architecture for supervised learning. The statistical model underlying the architecture hierarchical mixture model in which both the mixture coefficients and the mixture components are generalized linear models (GLIM's). Learning is treated as maximum likelihood problem: in particular, we present an Expectation Expectation-Muximizann Maximization (EM) algorithm for adjusting the parameters of the architecture. We also develop an on on-line learning algorithm in which the parameters are updated incrementally. Comparative simulation results are presented in the robot dynamics domain. This report describes research done at the Dept. of Brain and Cognitive Sciences, the Center for Biological and Computational Learning, and the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for CBCL is provided in part by grant from the NSF (ASC-921704)
Support for the laboratory's 's artificial intelligence research is provided in part by the Advanced Research Projects Agency of Dept. of Defense. The authors were supported by a grant from McDonnell- Pew Foundation, by grant from ATR Human Information Processing Research Laboratories, by a grant from Siemens Corporation, by by grant IRI-901399) from the National Science Foundation, by grant N00014 11001-90-J-1943 from the Office of Naval Research, and by NSF grant ECS-921653 to support an Initiative in Intelligent Control at MIT. Michael I. Jordan is NSF Presidential Young Investigator."
"neighbors":
166.
637.
761,
1004,
1005,
1 1116.
1196
J.
"mask": "Train"
).
}
46

---

Table 10: Semantic Enhancement Case Study
Dataset: <SubCCrr>>
Enhancement Agent generates high-quality nodes:
"node_id" "new_node 1",
"label": 0,
"text": "Title: Integrating Explanation- Learning with Case Adaptation Strategies' \n Abstract: This paper presents novel approach to case adaptation in case-based reasoning systems by integrating explanation-basse basedd learning techniques. Traditional case adaptation relies heavily on domain-specific adaptation rules that are often difficult to acquire and main- tain. Our approach uses explanations generated during problem-solving identify adaptation patterns and generalize them into reusable adaptation strategies. We demonstrate how these strategies can be applied across different domains with minimal knowledge engineering effort. Experimental results show that the integrated approach improves adaptation performance in comparison rule-based adaptation methods, especially domains where adaptation knowledge is incomplete or rapidly evolving. The paper presents formal framework for the approach and discusses its implementation case-based planning system.". "neighbors": [337, 833, 639, 476]. "mask": "Train"
"node id":
2",
"label": 1,
"text": "Title: Adaptive Parameter Control in Evolution Strategies for Dynamic Environments'v n Abstract: Omitted due to table size limitation.", "neighbors": [462, 70, 1312],
"mask": "Train"
mooee_dd", "new_node 3", "label": 0,
"text": "Title: Multi-Level Similarity Assessment for Case Retrieval in Heterogeneous Domains'y An Abstract: Omitted due to table size limitation.", "neighbors": [637, 638, 825, 1004), "mask": "Train"
"node_id" id": "new_node" 4",
"label": 3,
"text": "Title: Hybrid Neural-Symbolic Architecture for Interpretable Knowledge Extraction\n Abstract: Omitted due to table size limitation.". "neighbors": [1290, 263].
"mask": "Train"
"node_id" id": "new "hew_node> 5",
"label": 0,
"text": "Title: Case-base Design for Knowledge Discovery An Abstract: Case Based Rea- soning has proven be useful for AI systems. Our research introduces a new method called KDD-CBR (Knowledge Discovery through Database Case-Based Based Reasoning) which combines data mining with case bases for information retrieval and management. The system works by analyzing patterns in large datasets and then applies unique non- traditional methods for case storage. Unlike other approaches, we focus on pattern recognition instead of adapta- tion or similarity, which makes our approach completely novel in the field. Tests show this approach has better inference capability than other CBR techniques in some instances but worse in others. The implications for future research directions are significant and should be explored further with additional funding and more test cases. Additionally, we plan to integrate KDD-CBR with deep neural networks to further enhance performance on arbitrary datasets.". "
"neighbors": [462, 70, 1017],
47
"mask": "Train"

---

Table 11: Semantic Enhancement Case Study
Dataset: <SubCora>>
Evaluation Agent evaluates node quality and determines whether it has converged.:
"node_id id"; "new_node 1",
"label": 0,
"text": "Title: Integrating Explanation-Basees Learning with Case Adaptation Strategies' An Abstract: This paper presents novel approach to case adaptation case-based reasoning systems by integrating explanation- -aatedd learning techniques. Traditional case adaptation relies heavily on domain-specific adaptation rules that are often difficult to acquire and maintain. Our approach adaptation patterns and generalize them into reusable adaptation strategies. We demonstrate how these strategies be applied across different domains with minimal knowledge engineering effort. Experimental results show that the integrated approach improves adaptation performance in comparison rule-based adaptation methods, especially in domains where adaptation knowledge is incomplete or rapidly evolving. The paper presents formal framework for the approach and discusses its implementation case-based planning system.".
explanations generated during problem-solving to identify
"neighbors": [337, 833, 639, 476].
"mask": "Train"
"node id": "new_node 2",
"label": 1,
"text": "Title: Adaptive Parameter Control in Evolution Strategies for Dynamic Environments An Abstract: Omitted due to table size limitation.", "neighbors": [462, 70, 1312], "mask": "Train"
"node id":
3",
"label": 0,
Multi-Level Similarity Assessment for Case Retrieval in Heterogeneous Domains \n Abstract: Omitted due to table size limitation.", "neighbors": [637, 638, 825, 1004), "mask": "Train"
"text": "Title:
"node id":
4",
"label": 3,
"text": "Title: Hybrid Neural-Symbolic Architecture for Interpretable Knowledge Extraction An Abstract: Omitted due table size limitation.". "neighbors": [1290, 263],
"mask": "Train"
Summary: After checking the node quality based the background knowledge and environment report, delete new node 5.
According to the status check, the entire synthesis process has not converged and needs to continue to the next round enhancement. }
48


Reviews:
[{"review_id": "ayGD6hTad3", "reviewer": "Reviewer_cSFb", "summary": "GraphMaster introduces a hierarchical, retrieval-augmented generation (RAG) multi-agent framework—Manager, Perception, Enhancement, and Evaluation agents—for synthesizing text-attributed graphs under severe data constraints . It also proposes six “Sub” variants of standard benchmarks to rigorously evaluate data-limited performance and a dual-perspective interpretability assessment combining expert human judgments with Grassmann manifold analysis . Extensive experiments show GraphMaster outperforming both classic and recent LLM-based baselines across multiple datasets .", "questions": "1. How does GraphMaster perform on large-scale graphs exceeding typical LLM context windows, and can scalable strategies like FastGCN or GraphSAINT be integrated?\n2. Can you provide a detailed sensitivity analysis for critical hyperparameters (N, M%) to guide practitioners in tuning the framework?\n3. Could you summarize the architectural and prompting details of Mixed-LLM and Synthesis-LLM in the main text and compare against recent graph generation baselines?\n4. What were the criteria for selecting the 50 expert reviewers, and how did you ensure the reliability and unbiased nature of their traceability scores?", "limitations": "* The Personalized PageRank–based Perception Agent may undersample semantically important peripheral communities in highly skewed or heterogeneous graphs, leading to “blind spots.”\n* Quality evaluation depends heavily on the chosen LLM, posing challenges for deployment on smaller or resource-constrained models.\n* The multi-agent design incurs substantial inference overhead; practical deployment for very large graphs requires advanced batching and optimization techniques.\n* Privacy, fairness, and potential semantic biases in the synthesized data remain to be rigorously examined .", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n\n* **Novel multi-agent RAG paradigm:** Decomposes graph synthesis into specialized agents—Manager for strategy, Perception for subgraph retrieval, Enhancement for node/edge generation, and Evaluation for iterative quality control—addressing semantic coherence and structural integrity in a unified framework .\n* **Data-limited benchmark creation:** Introduces “Sub” variants of six widely used TAG datasets to test synthesis methods under realistic scarcity scenarios .\n* **Dual-perspective interpretability evaluation:** Combines quantitative Grassmann manifold metrics with qualitative human expert traceability scores (Tscore = 0.92) to validate semantic coherence and transparency .\n* **Strong theoretical foundations:** Provides convergence guarantees, information-theoretic bounds for the Perception Agent, and concentration results for the Enhancement and Evaluation agents .\n* **Comprehensive empirical validation:** Ablation studies, hyperparameter sweeps, and comparisons with both classic and newly crafted LLM-based baselines demonstrate state-of-the-art performance across six datasets and four GNN architectures .\n\nWeaknesses:\n\n\n1. **Scalability to very large graphs** (Section 3.6, lines 213–223): The time complexity analysis acknowledges batching needs for “extremely large graphs,” but no empirical evaluation is provided. Could the authors evaluate GraphMaster on larger-scale datasets or integrate scalable sampling/inference strategies [1,2] to demonstrate practical scalability?\n2. **Hyperparameter sensitivity** (Section 4.4, lines 316–323): Ablation shows optimal settings for N=30 and M=15%, but lacks guidance on robustness across other values. Could the authors include sensitivity analyses or contour plots to illustrate performance variation, perhaps leveraging [3,4].\n3. **Details of self-created LLM baselines** (Section 4.1, lines 243–249): The paper introduces Mixed-LLM and Synthesis-LLM but defers implementation specifics to the appendix, making direct comparison difficult. Could the authors summarize key architectural differences and prompt designs in the main text and compare against recent LLM-based graph generation methods [5,6].\n4. **Human evaluation methodology** (Section 4.3, lines 284–292): Expert traceability scores are reported, but the selection criteria and demographic breakdown of the 50 reviewers are not described. Could the authors detail reviewer expertise, sampling protocol, and bias mitigation measures?\n5. **Comparison errors and potential question** (Section 4.1, Table 1): The F1 score for LLM4NG in arXiv2023 dataset is 81.2, which is better than the proposed method GraphMaster (63.4). However, GraphMaster is still highlighted as the best method. In addition, how the baseline achieves a near 20 percent advantage over the GraphMaster is not properly justified.\n\n[1] Chen, Jie, Tengfei Ma, and Cao Xiao. \"FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling.\" International Conference on Learning Representations. 2018.\n\n[2] Zeng, Hanqing, et al. \"GraphSAINT: Graph Sampling Based Inductive Learning Method.\" International Conference on Learning Representations.\n\n[3] You, Jiaxuan, Zhitao Ying, and Jure Leskovec. \"Design space for graph neural networks.\" Advances in Neural Information Processing Systems 33 (2020): 17009-17021.\n\n[4] Yu, Tong, and Hong Zhu. \"Hyper-parameter optimization: A review of algorithms and applications.\" arXiv preprint arXiv:2003.05689 (2020).\n\n[5] Dadauto, Caio V., Nelson LS da Fonseca, and Ricardo da S. Torres. \"Data-driven intra-autonomous systems graph generator.\" IEEE Transactions on Network and Service Management (2024).\n\n[6] Liu, Chengyi, et al. \"Generative diffusion models on graphs: methods and applications.\" Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence. 2023.", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer cSFb for your insightful comments. We address each weakness (Wn), question (Qn), and limitation (Ln) point-by-point below. **All suggestions will be incorporated into the revised version.**\n\n**W1&Q1**: GraphMaster’s scalability is independent of graph size since **each LLM invocation handles a fixed-size capsule (N≈30)**, thus the per-iteration LLM cost is agnostic to node/edge counts. Only total LLM calls scale with dataset size, a natural trade-off. Experiments on **OGBN-ArXiv[1]** (169K nodes, 1.2M edges) confirm GraphMaster’s scalability, improving accuracy (82.3→85.2). However, we found that FastGCN and GraphSAINT indeed help GraphMaster. Integrating **GraphSAINT edge sampling** significantly enhances efficiency, replacing PPR to prioritize low-degree, semantically rich “weak ties”:\n$$\np_e=\\frac{|b_e|}{\\sum_{e’}|b_{e’}|}, \\quad b_e=\\frac{1}{\\deg(u)}+\\frac{1}{\\deg(v)}, \\quad p_v=\\sum_{e\\ni v}p_e\n$$\nThis seed-free method reduces variance and computational cost. Thank you for highlighting GraphSAINT, greatly boosting practical scalability.\n\n**W2 & Q2**: Sample size $N$ balances structural context and LLM token constraints. Information theory indicates information gain follows a diminishing returns curve:\n$$\nI(N) = \\frac{\\log(1 + \\alpha N)}{1 + \\beta N},\n$$\nOptimal $N$ consistently falls within [25,35] (α∈[0.1,0.3], β∈[0.01,0.04]).  Per **Theorem 2**, $M%$ governs semantic innovation vs coherence; extremes degrade performance. Empirically, best accuracy results (GCN/Cora) at $N=30$, $M=15%$: \n\n| $N\\backslash M\\%$ | 5%       | 10%      | 15%          | 20%      | 25%      |\n| ----------------- | -------- | -------- | ------------ | -------- | -------- |\n| **10**            | 89.3±0.9 | 89.5±0.8 | 89.1±0.9     | 88.1±1.0 | 87.4±1.1 |\n| **20**            | 90.1±0.8 | 91.4±0.7 | 91.5±0.8     | 91.0±0.9 | 89.2±1.0 |\n| **30**            | 90.8±0.7 | 92.3±0.9 | **92.6±0.5** | 92.0±0.8 | 90.5±0.9 |\n| **40**            | 91.0±0.8 | 91.8±0.8 | 92.2±0.9     | 91.8±0.9 | 89.8±1.1 |\n| **50**            | 90.7±0.9 | 91.6±0.9 | 92.0±1.0     | 91.2±1.0 | 88.9±1.2 |\n\nWe adopt $N=30$, $M=15%$ as default. Note optimal parameters may vary by LLM size—smaller models prefer lower values, larger ones higher.\n\n**W3&Q3**: We will summarize key baselines clearly in main text:\n\n- **Mixed-LLM**: Extends vision-inspired semantic interpolation, mixing node representations via Beta distribution for coherent boundary samples.\n- **Synthesis-LLM**: Combines local-context sampling (PPR+BFS), preserving local topological consistency.\n\nComparison to cited works:\n\n- **Dadauto et al.**: Uses GraphRNN to generate topology only, no textual attributes or LLM usage.\n- **Liu et al.**: Surveys graph diffusion models for molecules/proteins, not TAG-focused or LLM-based. Performance under data-limited conditions unknown.\n\n**W4&Q4:** We recruited 50 experts in graph ML/NLP/data mining (**inclusion: ≥2 peer-reviewed paper in the last three years or ≥2 years of relevant industrial R&D; all with potential COI were excluded**). The cohort comprised 38 PhD students, 6 postdocs/RAs, 3 faculty, and 3 industry researchers, with geographic distribution Asia 50%, Europe 20%, North America 24%, Others 6%. We evaluated 200 synthesized instances across six datasets, **each scored by 3 raters** on transparency, explainability, and predictability (1–5 Likert scale). Raters completed a **briefing and calibration (Cohen’s κ≥0.70)**. To mitigate bias, we adopted a double-blind setup, randomized item/order, used a standardized rubric, and removed inattentive raters. In our main study, we observed moderate-to-substantial inter-rater agreement (**average pairwise Cohen’s κ = 0.72 ± 0.05**). We will include the complete selection criteria, demographics, annotation protocol, and inter-rater reliability statistics in the appendix.\n\n**W5:** We apologize for typo in Table 1. **Corrected LLM4NG (arXiv2023): Accuracy:79.0, F1:61.2, below GraphMaster (Acc:87.9, F1:66.3)**. Multiple validations confirm correctness; table will be fixed.\n\n**L1:** We respectfully disagree with the assertion that our Personalized PageRank-based Perception Agent creates \"blind spots\" in highly skewed or heterogeneous graphs. Our design specifically addresses this concern through a deliberate **community-aware sampling strategy**.\n\nAs detailed in Section 3.3 and Equation (7), our Perception Agent employs a mode-adaptive seed selection strategy that explicitly prioritizes smaller communities when operating in semantic enhancement mode. Specifically, we select seed communities using:\n$$\nC_b = \\arg \\min_i |C_i| · (1 + μ · \\text{Var}({x_j : v_j ∈ C_i}))\n$$\nThis formulation inversely weights community size, ensuring peripheral communities have higher selection probability, directly counteracting traditional PPR biases.\n\nFurthermore, our semantic-enriched modularity maximization (Equation 6)\n$$\nQ_{\\text{sem}} = \\frac{1}{2m} \\sum_{i,j} \\left[ A_{ij} - \\gamma \\frac{k_i k_j}{2m} - (1 - \\gamma) \\frac{d_{\\text{sem}}(x_i, x_j)}{\\sum_{l,m} d_{\\text{sem}}(x_l, x_m)} \\right] \\delta(c_i, c_j)\n$$\n**balances both topological and semantic factors** through the parameter $γ$, ensuring that semantically coherent but structurally peripheral communities are properly identified and represented in the knowledge extraction process.\n\nThe experimental results in Figure 2 and Appendix H demonstrate that our approach successfully preserves both structural and semantic diversity across all tested datasets, including **highly heterogeneous graphs like Children and Arxiv2023**. The consistently high label homogeneity similarity scores (>0.96) across diverse graph structures provide empirical evidence that our method effectively captures peripheral semantic communities rather than creating blind spots.\n\n**L2:** Quality evaluation partially correlates with underlying LLM size, but careful generation-scale control enables small models (7B–8B, e.g., Qwen2, LLaMA3) to achieve near-parity with 32B models (1–2% difference on Sub-Cora, Sub-ArXiv-2023). To further decouple quality control from large models, we also adopt a lightweight geometric filter: embeddings of newly created nodes are projected onto a Grassmann manifold, and nodes whose distance to the task-specific centroid exceeds a learned threshold are discarded. This purely vector-space check only requires simple linear algebra and strongly correlates with human judgments **Together, these two measures—generation-scale moderation and Grassmann-manifold filtering—demonstrate that the Evaluation Agent can perform high-quality node evaluation even in resource-constrained environments, significantly reducing the dependence on very large LLMs.**\n\n**L3**: We appreciate the reviewers' insightful comments regarding the computational overhead of GraphMaster. This is indeed an important practical consideration that we take seriously.\n\n**However, we would like to emphasize that we have already made substantial efforts to reduce GraphMaster's computational cost**, which are manifested in the following aspects:\n\n1. **Efficient Knowledge Extraction**: Our Perception Agent reduces the input node count in each iteration by identifying the optimal external knowledge subset, significantly limiting the computational burden per round.\n2. **Environment Report Reuse**: The initial Environment Report generated by the Perception Agent can be stored and reused. In subsequent iterations, we only need to generate differential updates and compare them with the initial report, rather than regenerating the entire report from scratch.\n\n**We agree that further optimizations are possible.** We observe that during the iterative synthesis process, many subgraph structures and semantic patterns are repetitive. For instance, when the Perception Agent analyzes different parts of the graph, similar community structures frequently appear multiple times. By implementing a caching system that stores environmental reports based on structural features (such as node count, density, and label distribution), we can avoid redundant LLM calls. **Our experiments demonstrate that when employing the caching method, the number of Perception Agent calls is reduced by 46%.**\n\nFurthermore, we can optimize the model selection strategy. When computational resources are limited, smaller models (7B) can be utilized for quality evaluation or generation tasks. **As mentioned in our response to your Limitation 2**, we found that replacing the Evaluation Agent with Qwen2-7B or directly adopting the Grassmann manifold method for filtering still enables GraphMaster to exhibit excellent performance. These approaches can all reduce GraphMaster's computational consumption.\n\nNevertheless, achieving strong performance with large models inevitably requires sufficient computational resources. The trade-off between computational efficiency and synthesis quality is an inherent challenge in LLM-based approaches, and our framework provides the flexibility to navigate this trade-off based on available resources and application requirements.\n\n**L4:** Recent work has demonstrated that **current LLMs already possess a certain degree of capabilities in detecting privacy leaks, unfair treatment, and semantic bias in generated text [2]**. GraphMaster integrates ethical safeguards at two levels: (i) Enhancement Agent invokes built-in LLM safety mechanisms to pre-screen content, (ii) Evaluation Agent applies stricter post-generation filtering. Dual-perspective validation (human review + Grassmann consistency) substantiates data reasonableness. Nonetheless, advanced adversarial scenarios remain open challenges; in revision, we raise safety thresholds to strengthen ethical robustness under stronger threat models.\n\n> [1] Hu, W. et. al. Open Graph Benchmark: Datasets for Machine Learning on Graphs.\n>\n> [2] Y. Huang et. all, TrustLLM: Trustworthiness in Large Language Models."}, {"review_id": "VN73QZiDon", "reviewer": "Reviewer_NL19", "summary": "GraphMaster is a novel multi-agent framework for synthesizing text-attributed graphs (TAGs). The authors design a system of four LLM-based agents, Manager, Perception, Enhancement, and Evaluation, which collectively generate new graph nodes and edges in an iterative way. \nEvaluation is conducted across six benchmarks using multiple GNN architectures. The authors also introduce data-limited variants of standard datasets and a dual-perspective interpretability framework, incorporating human evaluation and Grassmann manifold-based semantic metrics. GraphMaster outperforms all baselines, including custom-built LLM synthesis methods.", "questions": "- How would GraphMaster perform on non-textual graphs (e.g., protein–protein interactions or sensor networks)?\n\n- Could this work be extended to multi-modal graphs?\n\n- How scalable is the multi-agent loop for large-scale graphs (>100K nodes), especially when multiple agents call the LLM iteratively?", "limitations": "Yes.", "rating": 6, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 4, "originality": 4, "strengths_and_weaknesses": "## Strenghts\n- They introduced the first TAG synthesis framework to use a modular, multi-agent LLM design under a retrieval-augmented generation (RAG) paradigm. \n\n- This paper has a comprehensive evaluation. They tested on six datasets and four GNNs (GCN, JKNet, GAT, GraphSAGE). The metrics used to evaluate are also extensive e.g. accuracy, F1, graph statistics.\n\n- The paper has theoretical foundations that are tightly linked to the agent design (Appendix J). \n- They have discussed time and space complexity analysis.\n\nThis paper is groundbreaking in its framework design, theoretical depth, and evaluation rigor. It sets a new direction for text-attributed graph synthesis and provides tools (metrics, datasets, proofs) that the community can build on.\n\n## Weaknesses\n- While the framework claims generality, the synthesis quality is not validated in physically meaningful graphs where semantics require domain knowledge (e.g., valid chemistry or logic).\n- This paper is hard to read and is quite dense. The writing could be improved.\n- The quality of Figure 2 and 3 could be improved.", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank Reviewer NL19 for your thoughtful and constructive feedback. We are especially grateful for your highly positive evaluation of our work. We address each of the weaknesses (refer as Wn) and questions (Qn) you raised point by point below. **All of your suggestions will be incorporated into the main text in the revised version.**\n\n**W2&W3:** We will substantially revise the manuscript to improve clarity and readability—simplifying dense passages, restructuring key sections, and refining language. We will also replace Figures 2 and 3 with higher-resolution, more legible, informative and beautiful versions in the next revision.\n\n**Q1&W1** : To address this Question, we have systematically adapted GraphMaster to  **Sociological Context (SoC), Protein–Protein Interactions, and Temporal Graphs.**\n\n**For Soc, We follow setting from GAG [1]**, creating GraphMaster-Soc for social network generation. We modified the Perception Agent to analyze social graph structures by incorporating social-specific community detection (focusing on follower clusters and interaction patterns rather than co-authorship), and redesigned the Enhancement Agent to generate diverse social interactions including tweets, likes, follows, retweets, and bookmarks as distinct edge types with corresponding semantic embeddings. The semantic-enriched modularity in Equation (6) was recalibrated with social homophily weights, while edge formation probabilities in Equation (11) were extended to handle multi-type social relationships ($\\theta_\\text{follow}$, $\\theta_\\text{like}$, $\\theta_\\text{retweet}$, etc.). We applied our M-Preserving Graph Sampling algorithm (Algorithm 1) to GAG's original SoC dataset, creating SubSoC variants that maintain essential social network properties while reducing scale by 90%. We then deployed GraphMaster-Soc on SubSoC and evaluated against the same baselines using GAG's established metrics. GraphMaster-Soc successfully generated social networks exhibiting key macroscopic properties (power-law degree distribution with α ∈ [1.96, 2.22], small-world characteristics with clustering coefficients **1300×** higher than random graphs), achieved superior microscopic structure alignment (6.4% improvement in GEM scores over best baselines), and maintained strong text-structure correlations with average ∆Acc improvements of $1.3_{±1.6}$ in GCN architectures on social node classification tasks. \n\n**For PPI settings, We utilize the SHS27K[3] dataset, a subset of STRING[2]**. Following the Sub data strategy from the original study, we randomly sample 10% of SHS27K to form the SubSHS27K subset, while the remaining 90% are regarded as unobserved data used to evaluate synthetic data quality.\n\nGraphMaster is employed for data synthesis, wherein each node $x_i$ maintains both protein sequences and functional descriptions. Protein sequences are converted into corresponding amino acids following a one-to-one mapping (e.g., A → Alanine), thus embedding amino acid and functional information within the node texts simultaneously. Additionally, the edge generation similarity metric $sim(x_s,x_i)$ from equation (11) in the original paper is modified from cosine similarity to sequence similarity (based on protein sequences), with all other components remaining unchanged. **This minimal modification enables GraphMaster to iteratively synthesize semantically coherent protein sequences within the protein interaction domain.** (Notably, protein interaction-specific LLMs would likely offer enhanced performance, and future explorations will develop a dedicated GraphMaster extension for protein interactions.)\n\nOur downstream task setup aligns exactly with Table 4 from **PIPR[4]**, utilizing accuracy across seven interaction classes to evaluate model performance. Post-synthesis, we adhere strictly to the experimental settings of the original PIPR study for training, benchmarking GraphMaster against two baselines to assess the practical benefit of synthetic data in extremely limited-sample scenarios. The results are as follows:\n\n| Methods                | Accuracy(%) |\n| ---------------------- | ----------- |\n| Zero rule              | 16.7        |\n| SubSHS27K              | 24.5 ± 1.3  |\n| SubSHS27K+Random Edges | 26.2 ± 2.4  |\n| SubSHS27K+GraphMaster  | 39.8 ± 0.9  |\n\nFor temporal Graph, we augment Eq. (7) with a time-aware sampling term:\n$$\nC_b = \\arg\\min_i |C_i| \\cdot \\bigl(1 + \\mu\\cdot\\mathrm{Var}(\\{x_j\\}) + \\nu\\cdot\\mathrm{Temp\\_Span}(C_i)\\bigr),\n$$\nwhere$ \\mathrm{Temp\\_Span}(C_i) = \\frac{t_{\\max}^{i} - t_{\\min}^{i}}{T_{\\max} - T_{\\min}}$ measures the temporal dispersion of edges within community $C_i$. Here, $t_{\\max}^{i} = \\max\\{\\,t_{\\text{end}} \\mid (u,v,t_{\\text{start}},t_{\\text{end}})\\in E,\\ u,v\\in C_i\\,\\}$ is the latest end time observed within the community, and $T_{\\max}, T_{\\min}$denote the global temporal bounds of the entire graph.\n\nWe further extend the edge-generation probability in Eq. (11) by adding a temporal consistency term $\\theta_4 \\cdot \\mathrm{Temp\\_Valid}(t, v_i)$ , where $\\mathrm{Temp\\_Valid}(t, v_i)$ computes the temporal precedence distance between the newly generated edge and the background edges adjacent to $v_i$, ensuring plausibility with respect to existing temporal patterns. Analogously, we incorporate a temporal consistency check into the filtering criterion in Eq. (12). In addition, we constrain the prompts to require timestamps, guiding the LLM to output timestamped KGs.\n\nUsing ICEWS [5], we construct **SubICEWS14** by applying the above sampling rule, and then evaluate with **CENET** [6]. We observe substantial gains after GraphMaster-based augmentation:\n\n|        Dataset         |  MRR  | Hits@1 | Hits@10 |\n| :--------------------: | :---: | :----: | :-----: |\n|       SubICEWS14       | 0.132 |  12.7  |  27.6   |\n| SubICEWS14+GraphMaster | 0.268 |  23.9  |  47.2   |\n\n**These results demonstrate strong adaptability: with only minimal architectural modifications, GraphMaster transfers effectively to the Soc, PPI and temporal domain and yields pronounced improvements.**\n\n**Q2**: We believe this is indeed a highly feasible and promising direction that aligns well with emerging trends in graph learning. Recent developments have introduced multi-modal graph datasets such as **MM-Graph [7] and MAGB [8]**, which contain not only textual attributes but also images, presenting similar data scarcity challenges as text-attributed graphs. **GraphMaster’s multi-agent architecture provides a natural foundation for multi-modal extension with minimal structural modifications.**\n\nSpecifically, we can **replace the base LLM with multi-modal large language models** (GPT-4V, LLaVA, or Qwen-VL) that can understand and generate both textual and visual content. The **Perception Agent** can initially leverage textual attributes for semantic-aware community detection and node sampling, and then enrich the extracted knowledge with corresponding visual attributes to provide a comprehensive multi-modal context.\n\nTo facilitate this, a practical approach is to employ a lightweight vision-language model (VLM) to generate **caption descriptions for each image**, which are then concatenated with the original textual attributes. This enriched textual input allows the Perception Agent to jointly reason over both modalities without fundamental architectural changes. We acknowledge that this is a preliminary design choice, and we recognize potential limitations in the expressiveness and faithfulness of generated captions. **Improving the Perception Agent’s multi-modal understanding capacity remains an open and important challenge for enabling high-fidelity multi-modal graph synthesis.**\n\nThe **Enhancement Agent** can then generate new nodes with coherent textual descriptions and corresponding images, while the **Evaluation Agent** assesses both intra-modal semantic coherence and cross-modal consistency. For datasets like MM-Graph, this would involve identifying representative subgraphs from existing text-image pairs, prompting multi-modal LLMs to synthesize new coherent multi-modal content, and evaluating cross-modal alignment using both human assessment and automated metrics.\n\nWe appreciate this insightful suggestion and consider multi-modal graph synthesis an exciting direction that we plan to pursue in future work.\n\n**Q3:** We emphasize that GraphMaster’s scalability is independent of graph size, since each LLM invocation is confined to a fixed-size retrieved capsule (N $\\approx$ 30), making the per-iteration LLM cost agnostic to the number of nodes and edges. The only aspect that scales with dataset size is the total number of LLM calls—and thus overall latency—which is a natural and acceptable trade-off. In our experiments on the large-scale **OGBN-ArXiv[9]** dataset (169,343 nodes, 1,166,243 edges), the GraphMaster-enhanced GCN sees its accuracy rise from 82.3 ± 0.1 to 85.2 ± 0.3, clearly demonstrating the model’s scalability.\n\n> [1] J Ji *et al.* LLM-Based Multi-Agent Systems are Scalable Graph Generative Models.\n>\n> [2] Szklarczyk, D. et al. The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest.\n>\n> [3] Szklarczyk et al. STRING v11: protein--protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets.\n>\n> [4] Chen M. et al. Multifaceted protein–protein interaction prediction based on Siamese residual RCNN.\n>\n> [5] E. Boschee et al. Icews coded event data, Harvard Dataverse.\n>\n> [6] Y. Xu et al, Temporal knowledge graph reasoning with historical contrastive learning.\n>\n> [7] J. Zhu,  et. al., Mosaic of Modalities: A Comprehensive Benchmark for Multimodal Graph Learning.\n>\n> [8] Yan, H. et al. When Graph meets Multimodal: Benchmarking and Meditating on Multimodal Attributed Graphs Learning.\n>\n> [9] Hu, W. et. al. Open Graph Benchmark: Datasets for Machine Learning on Graphs."}, {"review_id": "aBDznZ2q3Y", "reviewer": "Reviewer_Mweh", "summary": "This paper tackles the challenge that Graph Foundation Models face due to the lack of large, diverse graph datasets. Instead of naive structural heuristics, it leverages a retrieval-augmented, multi-agent LLM framework, comprising a Manager (to plan synthesis strategies), a Perception agent (to extract context-sized subgraphs), an Enhancement agent (to generate semantically rich nodes and edges), and an Evaluation agent (to iteratively assess and refine outputs) to produce text-attributed graphs with both structural fidelity and meaningful attributes. To validate this under realistic data scarcity, the authors introduce “Sub” variants of six standard graph benchmarks and propose a dual interpretability scheme combining expert human judgments with a Grassmannian-manifold analysis of embeddings. Experiments demonstrate that GraphMaster markedly outperforms traditional synthesis baselines in both semantic coherence and structural metrics, establishing a new paradigm for generating high-quality TAGs in low-data settings", "questions": "Could the authors clarify how GraphMaster could be scaled efficiently for larger graphs or more constrained compute environments? For example, do you consider strategies like caching previous LLM outputs, batching inputs, or employing smaller/distilled models?\n\nCould the authors clarify details about the human evaluation process--such as how raters were selected, how many raters participated, how many samples were rated, and inter-rater agreement statistics?", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "## Strengths\nThe authors don’t limit their evaluation to standard structural measures like degree distributions and clustering coefficients. Instead, they also bring in human evaluators to judge whether the generated graphs actually make sense semantically, and then back up these human judgments with a Grassmannian-manifold embedding analysis. By using both human feedback and embedding metrics, they convincingly show that GraphMaster produces graphs that are structurally accurate and semantically meaningful.\n\nAnother strong point is the realistic scenario tested in the paper. Instead of using artificial, overly simple datasets, the authors created reduced (\"Sub\") versions of six common benchmarks. This approach genuinely reflects real-world situations where only limited graph data is available, highlighting GraphMaster's potential usefulness for practical applications.\n\nFinally, the authors combine human-based evaluations with quantitative embedding analysis in a novel way. Human raters offer practical, intuitive insights about semantic quality, while the Grassmannian-manifold method gives a clear, quantitative measure of embedding similarity. Together, these two evaluations provide a balanced assessment of GraphMaster's effectiveness.\n\n## Weaknesses\nOne concern is the method’s computational cost. GraphMaster uses several large language model (LLM) agents repeatedly within a retrieval-augmented generation loop. Since the authors don’t discuss ways to potentially reduce these costs, like caching, batching, or using simplified models -- it remains unclear whether the method can scale efficiently or affordably for practical users dealing with larger graphs.\n\nThe human evaluation also lacks critical details. The authors don’t explain clearly how the evaluators were chosen, how many graphs they looked at, or if they generally agreed with each other. Without these details, it’s difficult to tell if the reported semantic coherence scores represent broad agreement or simply reflect a small, possibly biased set of examples.\n\nFinally, all experiments are limited to citation and academic graphs with textual attributes. The authors haven’t tested whether GraphMaster would work equally well on other types of graphs--like social networks, protein interaction networks, or knowledge graphs with more complex structures. Because of this, it’s hard to know how broadly theapproach could be applied beyond the specific types of graphs studied in the paper.", "was_revised": true, "final_justification": "Authors have addressed my concerns and give a clearly rebuttal.", "rebuttal": "We thank the reviewer Mweh for your insightful comments. We address each of the weaknesses (refer as Wn) and questions (Qn) you raised point by point below. **All of your suggestions will be incorporated into the main text in the revised version.**\n\n**W1&Q1**: We appreciate the reviewers' insightful comments regarding the computational overhead of GraphMaster. This is indeed an important practical consideration that we take seriously.\n\n**However, we would like to emphasize that we have already made substantial efforts to reduce GraphMaster's computational cost**, which are manifested in the following aspects:\n\n1. **Efficient Knowledge Extraction**: Our Perception Agent reduces the input node count in each iteration by identifying the optimal external knowledge subset, significantly limiting the computational burden per round.\n2. **Environment Report Reuse**: The initial Environment Report generated by the Perception Agent can be stored and reused. In subsequent iterations, we only need to generate differential updates and compare them with the initial report, rather than regenerating the entire report from scratch.\n\n**We agree that further optimizations are possible.** We observe that during the iterative synthesis process, many subgraph structures and semantic patterns are repetitive. For instance, when the Perception Agent analyzes different parts of the graph, similar community structures frequently appear multiple times. By implementing a caching system that stores environmental reports based on structural features (such as node count, density, and label distribution), we can avoid redundant LLM calls. **Our experiments demonstrate that when employing the caching method, the number of Perception Agent calls is reduced by 46%.**\n\nFurthermore, we can optimize the model selection strategy. When computational resources are limited, smaller models (7B) can be utilized for quality evaluation or generation tasks. **As mentioned in our response to Reviewer cSFb Limitation 2**, we found that replacing the Evaluation Agent with Qwen2-7B or directly adopting the Grassmann manifold method for filtering still enables GraphMaster to exhibit excellent performance. These approaches can all reduce GraphMaster's computational consumption.\n\n**Nevertheless, we want to emphasize that achieving strong performance with large language model inference inevitably requires adequate computational resources.** While various methods can reduce computational costs, sufficient computing power indeed enables GraphMaster to reach its peak performance. The trade-off between computational efficiency and synthesis quality is an inherent challenge in LLM-based approaches, and our framework provides the flexibility to navigate this trade-off based on available resources and application requirements.\n\n**W2&Q2**: We recruited 50 experts in graph ML/NLP/data mining **(inclusion: ≥2 peer-reviewed paper in the last three years or ≥2 years of relevant industrial R&D; all with potential COI were excluded)**. The cohort comprised 38 PhD students, 6 postdocs/RAs, 3 faculty, and 3 industry researchers, with geographic distribution Asia 50%, Europe 20%, North America 24%, Others 6%. We evaluated 200 synthesized instances across six datasets, each independently scored by 3 raters (total 600 ratings) on three dimensions—process transparency, decision explainability, and outcome predictability—using a 1–5 Likert scale. Raters completed a 30-minute briefing plus a 20-item calibration, and only those achieving Cohen’s κ ≥ 0.70 on the pilot were retained. To mitigate bias, we adopted a double-blind setup, randomized item/order, used a standardized rubric, and removed two raters who failed attention checks. In our main study, we observed moderate-to-substantial inter-rater agreement **(average pairwise Cohen’s κ = 0.72 ± 0.05)**. We will include the complete selection criteria, demographics, annotation protocol, and inter-rater reliability statistics in the appendix.\n\n**W3**: To address this Question, we have systematically adapted GraphMaster to  **Sociological Context (SoC), Protein–Protein Interactions, and Temporal Graphs.**\n\n**For Soc, We follow setting from GAG [1]**, creating GraphMaster-Soc for social network generation. We modified the Perception Agent to analyze social graph structures by incorporating social-specific community detection and redesigned the Enhancement Agent to generate diverse social interactions as distinct edge types with corresponding semantic embeddings. The semantic-enriched modularity in Equation (6) was recalibrated with social homophily weights, while edge formation probabilities in Equation (11) were extended to handle multi-type social relationships. Following our established data-limited methodology, we applied Algorithm 1 in our paper to GAG's original SoC dataset, creating SubSoC variants that maintain essential social network properties while reducing scale by 90%. We then deployed GraphMaster-Soc on these SubSoC datasets and evaluated against the same baselines using GAG's established metrics. GraphMaster-Soc successfully generated social networks exhibiting key macroscopic properties (power-law degree distribution with α ∈ [1.96, 2.22], small-world characteristics with clustering coefficients **1300×** higher than random graphs), achieved superior microscopic structure alignment (6.4% improvement in GEM scores over best baselines), and maintained strong text-structure correlations with average ∆Acc improvements of $1.3_{±1.6}$ in GCN architectures on social node classification tasks.\n\n**For PPI settings, We utilize the SHS27K[3] dataset, a subset of STRING[2]**. Following the Sub data strategy from the original study, we sample 10% of SHS27K to form the SubSHS27K subset.\n\nGraphMaster is employed for data synthesis, wherein each node $x_i$ maintains both protein sequences and functional descriptions. Protein sequences are converted into corresponding amino acids following a one-to-one mapping (e.g., A → Alanine), thus embedding amino acid and functional information within the node texts simultaneously. Additionally, the edge generation similarity metric $sim(x_s,x_i)$ from equation (11) in the original paper is modified from cosine similarity to sequence similarity (based on protein sequences), with all other components remaining unchanged. This minimal modification enables GraphMaster to iteratively synthesize semantically coherent protein sequences within the protein interaction domain. (Notably, protein interaction-specific LLMs would likely offer enhanced performance, and future explorations will develop a dedicated GraphMaster extension for protein interactions.)\n\nOur downstream task setup aligns exactly with Table 4 from **PIPR[4]**, utilizing accuracy across seven interaction classes to evaluate model performance. Post-synthesis, we adhere strictly to the experimental settings of the original PIPR study for training, benchmarking GraphMaster against two baselines to assess the practical benefit of synthetic data in extremely limited-sample scenarios. The results are as follows:\n\n| Methods                | Accuracy(%) |\n| ---------------------- | ----------- |\n| Zero rule              | 16.7        |\n| SubSHS27K              | 24.5 ± 1.3  |\n| SubSHS27K+Random Edges | 26.2 ± 2.4  |\n| SubSHS27K+GraphMaster  | 39.8 ± 0.9  |\n\nFor temporal graphs, we augment Eq. (7) with a time-aware sampling term:\n$$\nC_b = \\arg\\min_i |C_i| \\cdot \\bigl(1 + \\mu\\cdot\\mathrm{Var}(\\{x_j\\}) + \\nu\\cdot\\mathrm{Temp\\_Span}(C_i)\\bigr),\n$$\nwhere$ \\mathrm{Temp\\_Span}(C_i) = \\frac{t_{\\max}^{i} - t_{\\min}^{i}}{T_{\\max} - T_{\\min}}$ measures the temporal dispersion of edges within community $C_i$. Here, $t_{\\max}^{i} = \\max\\{\\,t_{\\text{end}} \\mid (u,v,t_{\\text{start}},t_{\\text{end}})\\in E,\\ u,v\\in C_i\\,\\}$ is the latest end time observed within the community, and $T_{\\max}, T_{\\min}$denote the global temporal bounds of the entire graph.\n\nWe further extend the edge-generation probability in Eq. (11) by adding a temporal consistency term $\\theta_4 \\cdot \\mathrm{Temp\\_Valid}(t, v_i)$ , where $\\mathrm{Temp\\_Valid}(t, v_i)$ computes the temporal precedence distance between the newly generated edge and the background edges adjacent to $v_i$, ensuring plausibility with respect to existing temporal patterns. Analogously, we incorporate a temporal consistency check into the filtering criterion in Eq. (12). In addition, we constrain the prompts to require timestamps, guiding the LLM to output timestamped KGs.\n\n**Using ICEWS [5], we construct SubICEWS14** by applying the above sampling rule, and then evaluate with **CENET** [6]. We observe substantial gains after GraphMaster-based augmentation:\n\n|        Dataset         |  MRR  | Hits@1 | Hits@10 |\n| :--------------------: | :---: | :----: | :-----: |\n|       SubICEWS14       | 0.132 |  12.7  |  27.6   |\n| SubICEWS14+GraphMaster | 0.268 |  23.9  |  47.2   |\n\n**These results demonstrate strong adaptability: with only minimal architectural modifications, GraphMaster transfers effectively to the SoC, PPI and temporal domain and yields pronounced improvements.**\n\n> [1] J Ji *et al.* LLM-Based Multi-Agent Systems are Scalable Graph Generative Models. Findings of ACL 2025.\n>\n> [2] Szklarczyk, D. et al. The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest. *Nucleic Acids Research*, 51(D1), D638–D646.\n>\n> [3] Szklarczyk et al. STRING v11: protein--protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets.\n>\n> [4] Chen M. et al. Multifaceted protein–protein interaction prediction based on Siamese residual RCNN. Bioinformatics\n>\n> [5] E. Boschee et al. Icews coded event data, Harvard Dataverse 12\n>\n> [6] Y. Xu et al, Temporal knowledge graph reasoning with historical contrastive learning,AAAI 2023."}, {"review_id": "y2milpFPCk", "reviewer": "Reviewer_RJSf", "summary": "GraphMaster introduces a novel multi-agent framework for synthesizing text-attributed graphs (TAGs) in data-limited environments. \nIt addresses challenges faced by existing LLMs in graph synthesis, such as context window limitations and hallucinations, by orchestrating four specialized LLM agents (Manager, Perception, Enhancement, and Evaluation).", "questions": "1. Can the framework be extended to synthesize more complex graph elements (e.g., hyper-edges, temporal dynamics) or entirely novel structural patterns beyond just nodes and edges?\n2. Current knowledge selection and LLM context limitations pose challenges for very large or highly heterogeneous graphs. In particular, the Perception Agent’s reliance on PPR to extract subgraphs may introduce sampling bias, favoring structurally central nodes while potentially missing peripheral but semantically important regions. Evaluating the approach on larger and more complex graphs would help clarify its scalability and generality.", "limitations": "Yes", "rating": 5, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### Strengths\n1. First multi-agent LLM framework specifically for data-limited TAG synthesis.\n2. Introduces a dual-perspective interpretability assessment combining human evaluation with Grassmannian manifold analysis.\n\n### Weaknesses\n1. Current knowledge selection and LLM context limitations pose challenges for very large or highly heterogeneous graphs.  \n2. The multi-agent architecture incurs substantial inference overhead due to multiple LLM calls per round.", "was_revised": true, "final_justification": "I have checked the response and decide to raised my score.", "rebuttal": "We thank the reviewer RJSf for your insightful comments. We address each of the weaknesses (refer as Wn) and questions (Qn) you raised point by point below. **All of your suggestions will be incorporated into the main text in the revised version.**\n\n**Q1**:  To address this Question, we have systematically adapted GraphMaster to  **Sociological Context (SoC), Protein–Protein Interactions, and Temporal Graphs.**\n\n**For Soc, We follow setting from GAG [1]**, creating GraphMaster-Soc for social network generation. We modified the Perception Agent to analyze social graph structures by incorporating social-specific community detection, and redesigned the Enhancement Agent to generate diverse social interactions as distinct edge types with corresponding semantic embeddings. The semantic-enriched modularity in Equation (6) was recalibrated with social homophily weights, while edge formation probabilities in Equation (11) were extended to handle multi-type social relationships. We create data-limited SubSoC datasets, then deployed GraphMaster-Soc on these SubSoC datasets and evaluated against the same baselines using GAG's established metrics. GraphMaster-Soc successfully generated social networks exhibiting key macroscopic properties (power-law degree distribution with α ∈ [1.96, 2.22], small-world characteristics with clustering coefficients **1300×** higher than random graphs), achieved superior microscopic structure alignment (6.4% improvement in GEM scores over best baselines), and maintained strong text-structure correlations with average ∆Acc improvements of $1.3_{±1.6}$ in GCN on social NC tasks.\n\n**For PPI settings, We utilize the SHS27K[3] dataset, a subset of STRING[2]**.  Following the Sub data strategy from the original study, we sample 10% of SHS27K.\n\nGraphMaster is employed for data synthesis, wherein each node $x_i$ maintains both protein sequences and functional descriptions. Protein sequences are converted into corresponding amino acids following a one-to-one mapping (e.g., A → Alanine), thus embedding amino acid and functional information within the node texts simultaneously. Additionally, the edge generation similarity metric $sim(x_s,x_i)$ from equation (11) in the original paper is modified from cosine similarity to sequence similarity (based on protein sequences), with all other components remaining unchanged. \n\nOur downstream task setup aligns exactly with **Table 4 from PIPR[4]**, utilizing accuracy across seven interaction classes to evaluate model performance. Post-synthesis, we adhere strictly to the experimental settings of the original PIPR study for training, benchmarking GraphMaster against two baselines to assess the practical benefit of synthetic data in extremely limited-sample scenarios. The results are as follows:\n\n| Methods                | Accuracy(%) |\n| ---------------------- | ----------- |\n| Zero rule              | 16.7        |\n| SubSHS27K              | 24.5 ± 1.3  |\n| SubSHS27K+Random Edges | 26.2 ± 2.4  |\n| SubSHS27K+GraphMaster  | 39.8 ± 0.9  |\n\nFor temporal graphs, we augment Eq. (7) with a time-aware sampling term:\n$$\nC_b = \\arg\\min_i |C_i| \\cdot \\bigl(1 + \\mu\\cdot\\mathrm{Var}(\\{x_j\\}) + \\nu\\cdot\\mathrm{Temp\\_Span}(C_i)\\bigr),\n$$\n\n\nwhere $ \\mathrm{Temp\\_Span}(C_i) = \\frac{t_{\\max}^{i} - t_{\\min}^{i}}{T_{\\max} - T_{\\min}}$ measures the temporal dispersion of edges within community $C_i$. Here, $t_{\\max}^{i} = \\max\\{\\,t_{\\text{end}} \\mid (u,v,t_{\\text{start}},t_{\\text{end}})\\in E,\\ u,v\\in C_i\\,\\}$ is the latest end time observed within the community, and $T_{\\max}, T_{\\min}$denote the global temporal bounds of the entire graph.\n\nWe further extend the edge-generation probability in Eq. (11) by adding a temporal consistency term $\\theta_4 \\cdot \\mathrm{Temp\\_Valid}(t, v_i)$ , where $\\mathrm{Temp\\_Valid}(t, v_i)$ computes the temporal precedence distance between the newly generated edge and the background edges adjacent to $v_i$, ensuring plausibility with respect to existing temporal patterns. Analogously, we incorporate a temporal consistency check into the filtering criterion in Eq. (12). In addition, we constrain the prompts to require timestamps, guiding the LLM to output timestamped KGs.\n\nUsing ICEWS [5], we construct **SubICEWS14** by applying the above sampling rule, and then evaluate with **CENET** [6]. We observe substantial gains after GraphMaster-based augmentation:\n\n|        Dataset         |  MRR  | Hits@1 | Hits@10 |\n| :--------------------: | :---: | :----: | :-----: |\n|       SubICEWS14       | 0.132 |  12.7  |  27.6   |\n| SubICEWS14+GraphMaster | 0.268 |  23.9  |  47.2   |\n\n**These results demonstrate strong adaptability: with only minimal architectural modifications, GraphMaster transfers effectively to the Soc, PPI and temporal domain and yields pronounced improvements.**\n\n**Re W1 and Q2:** **For very large graphs**, GraphMaster's scalability is independent of graph size, since each LLM invocation is confined to a fixed-size retrieved capsule (N≈30), making the per-iteration LLM cost agnostic to the number of nodes and edges. The only aspect that scales with dataset size is the total number of LLM calls--and thus overall latency--which is a natural and acceptable trade-off. In our experiments on large-scale datasets like OGBN-ArXiv[7] (169,343 nodes, 1,166,243 edges), GraphMaster demonstrates clear scalability with accuracy improvements from 82.3±0.1 to 85.2±0.3.\n\n**For highly heterogeneous graphs**, GraphMaster's multi-agent architecture proves highly effective through several key mechanisms. First, our Perception Agent employs a community-aware sampling strategy that deliberately prioritizes smaller, peripheral communities as PPR starting points (Eq.7), directly counteracting potential sampling bias toward large, well-connected regions and ensuring semantically important but structurally sparse areas receive proper attention. Second, we designed semantic and topological enhancement modes that adaptively switch based on the current graph composition, enabling GraphMaster to progressively address different types of heterogeneity across multiple synthesis rounds--early iterations may focus on dominant node types, while later rounds target underrepresented semantic clusters. Furthermore, the powerful cross-domain knowledge of existing LLMs enables coherent synthesis even when bridging disparate semantic regions. While extremely heterogeneous graphs may require more synthesis rounds to achieve optimal coverage, our experimental results on diverse datasets like Children demonstrate that GraphMaster maintains both semantic coherence and structural integrity across heterogeneous regions.\n\n**Re W2**: This is indeed an important practical consideration that we take seriously.\n\n**However, we would like to emphasize that we have already made substantial efforts to reduce GraphMaster's computational cost**, which are manifested in the following aspects:\n\n1. **Efficient Knowledge Extraction**: Our Perception Agent reduces the input node count in each iteration by identifying the optimal external knowledge subset, significantly limiting the computational burden per round.\n2. **Environment Report Reuse**: The initial Environment Report generated by the Perception Agent can be stored and reused. In subsequent iterations, we only need to generate differential updates and compare them with the initial report, rather than regenerating the entire report from scratch.\n\n**We agree that further optimizations are possible.** We observe that during the iterative synthesis process, many subgraph structures and semantic patterns are repetitive. For instance, when the Perception Agent analyzes different parts of the graph, similar community structures frequently appear multiple times. By implementing a caching system that stores environmental reports based on structural features (such as node count, density, and label distribution), we can avoid redundant LLM calls. **Our experiments demonstrate that when employing the caching method, the number of Perception Agent calls is reduced by 46%.**\n\nFurthermore, we can optimize the model selection strategy. When computational resources are limited, smaller models (7B) can be utilized for quality evaluation or generation tasks. **As mentioned in our response to Reviewer cSFb Limitation 2**, we found that replacing the Evaluation Agent with Qwen2-7B or directly adopting the Grassmann manifold method for filtering still enables GraphMaster to exhibit excellent performance. These approaches can all reduce GraphMaster's computational consumption.\n\n**Nevertheless, we want to emphasize that achieving strong performance with large language model inference inevitably requires adequate computational resources.** While various methods can reduce computational costs, sufficient computing power indeed enables GraphMaster to reach its peak performance. The trade-off between computational efficiency and synthesis quality is an inherent challenge in LLM-based approaches, and our framework provides the flexibility to navigate this trade-off based on available resources and application requirements.\n\n> [1] J Ji *et al.* LLM-Based Multi-Agent Systems are Scalable Graph Generative Models.\n>\n> [2] Szklarczyk, D. et al. The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest. \n>\n> [3] Szklarczyk et al. STRING v11: protein--protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets.\n>\n> [4] Chen M. et al. Multifaceted protein–protein interaction prediction based on Siamese residual RCNN. \n>\n> [5] E. Boschee et al. Icews coded event data.\n>\n> [6] Y. Xu et al, Temporal knowledge graph reasoning with historical contrastive learning.\n>\n> [7] Hu, W. et. al. Open Graph Benchmark: Datasets for Machine Learning on Graphs."}]

Author rebuttal:

Meta-review: The paper presents GraphMaster, a hierarchical, retrieval-augmented generation (RAG) multi-agent framework, comprising agents corresponding to Manager, Perception, Enhancement, and Evaluation agents, for synthesizing text-attributed graphs under data constraints. The work further introduces 6 benchmark variants to rigorously assess data-limited performance and proposes a novel dual-perspective interpretability evaluation that combines expert human judgments with Grassmann manifold analysis.

The contributions are well-motivated and methodologically sound, with extensive experiments showing that GraphMaster consistently outperforms both classic graph generation methods and recent LLM-based baselines across multiple datasets. 

Three of the four reviewers express support for acceptance, with one describing the work as "ground-breaking." Reviewer cSFb assigned a score of 3 but did not participate in the rebuttal discussion despite multiple reminders. After reviewing the authors’ rebuttal and the discussion, I find no substantive concerns that would justify overturning the otherwise positive consensus.

I recommend GraphMaster for a spotlight presentation due to its innovative methodological and empirical contributions to graph generation. This is the first multi-agent LLM framework for graph synthesis that goes beyond one-shot generation and demonstrates how specialized LLM reasoning can improve structured data generation.

The work also extends retrieval-augmented generation (RAG) to graph synthesis and uniquely achieves dual optimization of both structural and textual attributes, a gap left unaddressed in prior work. Its dual-view interpretability evaluation, combining expert human judgments with Grassmann manifold–based geometric analysis are also highly innovative.

The paper’s completeness is exemplary: detailed theoretical analysis (Appendices I & J), extensive ablations, reproducibility artifacts, and carefully constructed baselines. Together, these qualities make GraphMaster not only original but also professionally polished and impactful.
Decision: Accept (spotlight)