LLMs for Graph Learning

LLM: Large Language Model

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7 papers in the last four weeks, up 40% on the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 42

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GraphMAS: A Systematic Benchmark of Multi-Agent Coordination for Graph Learning
Sep 30, 2026Jiayi Yang, Yifang Chen, Yuanfu Sun +2Multi-Agent Coordination Text-Attributed Graph Learning
New York University Shanghai · New York University · Northwestern University
ZeroGAR: Benchmarking the Adversarial Robustness of Zero-Shot Graph Models
Sep 27, 2026Zhongjian Zhang, Xiao Wang, Busheng Zhang +5Graph Representation Learning Adversarial Attacks
Beijing University of Posts and Telecommunications · Beihang University · Tsinghua University +2
ABSOL: Aggregated Bayesian Subsampling Orchestrated with LLMs
Sep 14, 2026Jackson Hassell, Chen Shen, Estevam HruschkaGraph Structure Learning Bayesian Networks
Megagon Labs
Structurally Speaking: Motif-Oriented Graph Captioning through Bidirectional Graph-Text Translation
Sep 10, 2026Hsiao-Ying Lu, Dongyu Liu, Kwan-Liu MaStructured Prompting Prompt Engineering
Department of Computer Science, University of California, Davis
Chimaera: A Mixture-of-Graph-Experts Architecture for Cross-Task and Cross-Dataset Graph Learning
Sep 8, 2026Jonathan Frank, David Richerby, Ansgar ScherpGraph Neural Networks Text-Attributed Graph Learning
University of Ulm · University of Essex
TTGBench: Benchmarking Topological Evolution and Semantic Drift in Text-attributed Temporal Graphs
Sep 8, 2026Longfei Ma, Zemin Liu, Fei WuTemporal GNNs Temporal Link Prediction
1Zhejiang University
LLMs for Social Network Modeling: From Network Generation to Dynamic Processes
Sep 7, 2026Shikha Mallick, Alex Thomo, Akrati SaxenaComputational Social Science LLMs for Graph Learning
Department of Computer Science, University of Victoria, BC, Canada · Leiden Institute of Advanced Computer Science, Leiden University, The Netherlands
Language-encoded network topology enables large language models to reason about complex networks
Sep 3, 2026Ucchwas Talukder Utsha, Sakib Mostafa, James Zou +1Graph Representation Learning LLM Reasoning with Graphs
Department of Radiation Oncology, Stanford University, Stanford, California, USA · Department of Electrical Engineering, Stanford University, Stanford, California, USA · Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, California, USA +1
CoMPASS: Collaborative Molecular Property Prediction via Adaptive Small-Large Model Synergy
Aug 31, 2026Wentao Li, Jiangjie Qiu, Yijun Li +2Graph Neural Networks Language Model Calibration
Beijing Key Laboratory of Artificial Intelligence for Advanced Chemical Engineering Materials · State Key Laboratory of Chemical Engineering and Low-Carbon Technology · Department of Chemical Engineering, Tsinghua University
Graph Evidence Is Not Enough: Diagnosing Native Decoder Use in Graph-Augmented LLMs
Aug 31, 2026Xiaoyu Guo, Pengcheng Chen, Jiong Yu +3LLM Reasoning with Graphs LLM Reasoning
School of Software, Xinjiang University, Urumqi 830002, China · School of Computer Science and Technology, Xinjiang University, Urumqi 830046, China
Auditing Privacy Risks in LLM-Enhanced Graph Neural Networks
Aug 26, 2026Longzhu He, Zelang Wen, Chaozhuo Li +1Privacy-Preserving ML LLMs for Graph Learning
Beijing University of Posts and Telecommunications
Multi-Granular Rationale-Guided Molecular LLM for Property Prediction
Aug 11, 2026Junwoo Park, Minyoung Shin, Cheol Soon Lee +1Molecular Property Prediction Molecular Representation Learning
Sungkyunkwan University · Korea University
Unified Multi-Dimensional Benchmark for Complex Graph Reasoning in Large Language Models
Aug 2, 2026Fali Wang, Ali Al-Lawati, Iliyas Bektas +5LLM Evaluation LLM Reasoning with Graphs
The Pennsylvania State University, University Park, PA, USA · Rensselaer Polytechnic Institute, Troy, NY, US
Interpretable Unsupervised Community Detection with LLM-Symbolized Structured Processes
Aug 2, 2026Aoting Zeng, Kai Wang, Jianwei Wang +3Unsupervised Learning Interpretable ML
Shanghai Jiao Tong University Shanghai, China · University of New South Wales Sydney, Australia
Agentic Graph Token Reasoning
Aug 1, 2026Zhuoyi Peng, Yi YangLLM Reasoning with Graphs Text-Attributed Graph Learning
The Hong Kong University of Science and Technology
Dual-Path LLM Reasoning for Multimodal Few-Shot Knowledge Graph Completion
Jul 29, 2026Jinlan Liu, Zhiying Tu, Yongchao Xing +5Multimodal Graph Learning Multimodal KGs
Harbin Institute of Technology (Weihai), Weihai 264209, China · Shandong Provincial Key Laboratory of Digital Service Computing Technology and Systems, Weihai 264200, China · Harbin Institute of Technology Qingdao Research Institute, Qingdao 266400, China
CHARM: A Multimodal Graph Foundation Model with Hierarchical Context Modeling for Zero-Shot Transfer
Jul 28, 2026Ankang Yang, Jitao Zhao, Di Jin +2Transfer Learning Graph Foundation Models
School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University · Data Science Program, Columbian College of Arts and Sciences, The George Washington University
FedLSG: LLM-Enhanced Semantic Calibration for Federated Graph Backdoor Defense
Jul 22, 2026Chenyu Zhou, Yabin Peng, Wei Huang +3Graph Neural Networks Backdoor Attacks
Southeast University Purple Mountain Laboratories · Institute of AI for Industries Chinese Academy of Sciences · Purple Mountain Laboratories
UNIT: Unleash Large Language Models Potential for Graph Continual Learning
Jul 11, 2026Tairan Huang, Yili Wang, Beibei Hu +4Graph Representation Learning Continual Learning
Central South University Changsha, China · Hongkong University of Science and Technology (Guangzhou) Guangzhou, China · Hunan Institute of Engineering Changsha, Hunan
TAG-DLM: Diffusion Language Models for Text-Attributed Graph Learning
Jun 30, 2026Lingjie Chen, Yuanchen Bei, Haobo Xu +3Masked Diffusion Models Link Prediction
UIUC · VISA
Enhancing LLMs for Graph Tasks via Graph-aware LoRA Generation
Jun 21, 2026Junshu Sun, Wanxing Chang, Qingming Huang +1Graph Representation Learning Low-Rank Adaptation
State Key Lab. of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China · University of Chinese Academy of Sciences, Beijing, China · DAMO Academy, Alibaba Group, Hangzhou, China
LLM Features Can Hurt GNNs: Concatenation Interference on Homophilous Graph Benchmarks
Jun 16, 2026Zhongyuan Wang, Pratyusha VemuriGraph Neural Networks Graph Representation Learning
AdaSTORM: Scaling LLM Reasoning on Dynamic Graphs via Adaptive Spatio-Temporal Multi-Agent Collaboration
Jun 15, 2026Bing Hao, Ruijie Wang, Haodong Qian +5LLM Reasoning with Graphs Multi-Agent Collaboration
1Tianjin University, China · 2Beihang University, China
Formalizing and Mitigating Structural Distortion in LLM Attention for Graph Reasoning
Jun 14, 2026Donald Loveland, Puja Trivedi, Ari Weinstein +2LLM Reasoning with Graphs Transformer Attention
University of Michigan Ann Arbor, MI, USA · Amazon Palo Alto, CA, USA
Beyond the Golden Teacher: Enhancing Graph Learning through LLM-GNN Co-teaching
Jun 10, 2026Zhuoyi Peng, Hanlin Gu, Lixin Fan +1Graph Representation Learning Text-Attributed Graph Learning
1The Hong Kong University of Science and Technology · 2WeBank
GraphInfer-Bench: Benchmarking LLM's Inference Capability on Graphs
Jun 10, 2026Zhuoyi Peng, Jingzhou Jiang, Hanlin Gu +2LLM Evaluation Graph Neural Networks
The Hong Kong University of Science and Technology · Webank
Are Large Language Models Suitable for Graph Computation? Progress and Prospects
Jun 5, 2026Yuting Zhang, Yi Han, Kai Wang +3LLM Reasoning with Graphs LLM Tool Use
University of New South Wales · Antai College of Economics and Management, Shanghai Jiao Tong University · Edith Cowan University +1
When Graph Tokens Sink: A Mechanistic Analysis of Graph Language Models
Jun 2, 2026Ding Zhang, Runtao Zhou, Wenqing Zheng +3LLM Interpretability Mechanistic Interpretability
University of Virginia · Capital One
Let Relations Speak: An End-to-End LLM-GNN Soft Prompt Framework for Fraud Detection
May 27, 2026Zhixing Zuo, Huilin He, Jiasheng Wu +1Graph Neural Networks Graph Representation Learning
School of Computer Science and Technology, Tongji University
Where LLM Annotators Fail: Label-Free Learning on Graphs with LLMs
May 27, 2026Safal Thapaliya, Jiatan Huang, Chuxu ZhangLLM-Assisted Annotation Text-Attributed Graph Learning
University of Connecticut, USA

Sep 30, 2026, cs.LGJ/K move · Enter open · S save

GraphMAS: A Systematic Benchmark of Multi-Agent Coordination for Graph Learning

Jiayi Yang, Yifang Chen, Yuanfu Sun, Xinyan Ge+1

New York University Shanghai · New York University · Northwestern University

Multi-Agent Coordination Text-Attributed Graph Learning LLMs for Graph Learning

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LLM-based multi-agent systems coordinate specialized reasoning through aggregation, interaction, and adaptive control, yet their potential for graph learning remains unexplored. Graph learning is a natural setting for such systems because useful evidence may arise from heterogeneous local, long-range, global structural, and semantic perspectives whose relevance varies across instances. Existing LLM-based graph learning approaches primarily rely on single-agent reasoning, while multi-agent coordination has been studied mainly in general reasoning settings. Consequently, it remains unclear whether multiple specialized agents can improve graph learning and how coordination strategies should be designed and evaluated. To address this gap, we introduce GraphMAS, a systematic benchmark of multi-agent coordination for graph learning. GraphMAS builds a shared pool of graph reasoning specialists and organizes coordination along two dimensions, inter-agent interaction and runtime adaptivity, yielding four paradigms and seven representative coordination methods. Under a unified protocol, we evaluate these methods across seven text-attributed graphs, three domains, and two graph learning tasks. We find that heterogeneous graph perspectives are complementary, and that coordinating specialists improves over individual specialists and single-agent graph reasoning, with gains from decomposing reasoning across specialists rather than from broader evidence access alone. However, richer inter-agent interaction does not reliably help, whereas instance-adaptive specialist selection yields the strongest accuracy-efficiency trade-off. We further show that coordination can be learned over a fixed specialist pool and transfers to held-out graphs. GraphMAS therefore provides a controlled evaluation framework and empirical principles for understanding when and how multi-agent coordination benefits graph learning.

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