Text-Attributed Graph Learning

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2 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

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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
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
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
ProTAGAD: A Foundation Model for TAG Anomaly Detection with Decoupled Topological and Textual Prototypes
Aug 11, 2026Ziyan Wang, Liwen Wu, Cheng Xie +3Prototype Learning Text-Attributed Graph Learning
School of Software and AI, Yunnan University, Kunming, China
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
One Model, Many Graphs: Learning over Attributed Graphs across Heterogeneous Modalities with Vision-Language Models
Jul 21, 2026Jiayi Yang, Yifang Chen, Yuanfu Sun +2Graph Representation Learning VLM Adaptation
New York University Shanghai · New York University
OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation
Jul 21, 2026Yuze Dai, Zhihan Zhang, Yan Zhao +6Text-Attributed Graph Learning Learning with Noisy Labels
Beijing Institute of Technology
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
PromptGNN-sim: Deep Fusion and Alignment of GNN and LLMs for Text-Attributed Graph Learning
Jun 29, 2026Zhifei Hu, Alexandra I. CristeaText-Attributed Graph Learning Multimodal Fusion
Durham University, Durham, United Kingdom
Node-to-Neighborhood Semantic Consistency: Text-Topology Alignment for TAGs Anomaly Detection
Jun 29, 2026Bochen Lin, Jianxiang Yu, Jiayi Wu +3Text-Attributed Graph Learning Anomaly Detection
School of Data Science and Engineering, East China Normal University, Shanghai, China · WeChat, Tencent, Guangzhou, China
TaLK: Text-attributed Graph Dataset Distillation via Coupling Language Model with Graph-Aware Kernel
Jun 22, 2026Yeongho Kim, Yeonje Choi, Kijung ShinText-Attributed Graph Learning Dataset Distillation
Kim Jaechul Graduate School of AI, KAIST
LLM Features Can Hurt GNNs: Concatenation Interference on Homophilous Graph Benchmarks
Jun 16, 2026Zhongyuan Wang, Pratyusha VemuriGraph Neural Networks Graph Representation Learning
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
GraspLLM: Towards Zero-Shot Generalization on Text-Attributed Graphs with LLMs
Jun 10, 2026Hengyi Feng, Zeang Sheng, Meiyi Qiang +2Graph Representation Learning Text-Attributed Graph Learning
University of Electronic Science and Technology of China · Peking University · Tencent Inc +1
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
ERAlign: Energy-based Representation Alignment of GNNs and LLMs on Text-attributed Graphs
Jun 9, 2026Xianlin Zeng, Fan Xia, Xiangyu ChenGraph Representation Learning Cross-Modal Alignment
Postdoctoral Research Station at China RongTong Academy of Sciences Group Corporation Limited, Beijing, China · Duke University, Durham, NC, USA.
Text-attributed Graph Condensation via Text Selection and Attribute Matching
Jun 2, 2026Haowei Han, Yuxiang Wang, Guojia Wan +5Graph Neural Networks Text-Attributed Graph Learning
School of Computer Science Wuhan University Wuhan, China · Institute for Math & AI Wuhan University Wuhan, China
G2LoRA: Gradient Orthogonal Low-Rank Adaptation Framework for Graph Continual Learning on Text-Attributed Graphs
Jun 1, 2026Yuhan Wang, Yibo Ding, Yutong Ye +4Continual Learning Low-Rank Adaptation
School of Computer Science and Engineering, Beihang University Beijing, China · Department of Statistics, Columbia University New York, NY, USA · College of Computer Science, Beijing University of Technology Beijing, China
GraphLit: Learning Text-Enriched Dynamic Character Network Representations for Literary Study
May 27, 2026Gaspard Michel, Elena V. Epure, Romain Hennequin +2Computational Literary Studies Heterogeneous GNNs
Deezer Research, Paris, France · IDIAP, Martigny, Switzerland · Loria, Nancy, France +1
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
Clustering as Reasoning: A $k$ -Means Interpretation of Chain-of-Thought Graph Learning
May 24, 2026Xuanting Xie, Zhaochen Guo, Bingheng Li +4Graph Representation Learning Text-Attributed Graph Learning
University of Electronic Science and Technology of China, 611731, Chengdu, China · Singapore Management University, 188065, Singapore · Michigan State University, East Lansing, 48824, MI, USA +1
CAMERA: Adapting to Semantic Camouflage in Unsupervised Text-Attributed Graph Fraud Detection
May 19, 2026Junjun Pan, Yixin Liu, Yu Zheng +3Text-Attributed Graph Learning Unsupervised Anomaly Detection
School of Information and Communication Technology, Griffith University, Australia · Department of Computer Science and Information Technology, La Trobe University, Australia
TERGAD: Structure-Aware Text-Enhanced Representations for Graph Anomaly Detection
May 19, 2026Wen Shi, Zhe Wang, Huafei Huang +6Graph Representation Learning Text-Attributed Graph Learning
College of Computer Science and Technology, Jilin University, Changchun 130012, China · School of Computer Science and Information Technology, Adelaide University, Adelaide SA5095, Australia · School of Computing Technologies, RMIT University, Melbourne, VIC 3000, Australia +1
S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs
May 18, 2026Yuhan Wang, Haopeng Zhang, Yibo Ding +6Cross-Modal Alignment Text-Attributed Graph Learning
Beihang University, Beijing, China · Tianjin University, Tianjin, China
Teaching LLMs to See Graphs: Unifying Text and Structural Reasoning
May 11, 2026Dario VajdaLLM Reasoning with Graphs Text-Attributed Graph Learning
Faculty of Computer and Information Science University of Ljubljana
PRISM: Iterative Cross-Modal Posterior Refinement for Dynamic Text-Attributed Graphs
May 7, 2026Trimble Chang, Yihang Liu, Mingjing Han +1Cross-Modal Representation Learning Temporal Link Prediction
College of Artificial Intelligence Nankai University
Robustness of Graph Self-Supervised Learning to Real-World Noise: A Case Study on Text-Driven Biomedical Graphs
May 6, 2026Othmane Kabal, Mounira Harzallah, Fabrice Guillet +2Graph SSL Text-Attributed Graph Learning
Nantes University, LS2N, Nantes, 44300, France. · National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku,2026 Tokyo 101-8430 Japan. · Institute of Science Tokyo, Tokyo 152-8550 Japan.
ModTGCN: Modularity-aware Graph Neural Networks for Text Classification
Apr 29, 2026Rajarshi Misra, Aditya Sharma, Vinti Agarwal +1Graph Neural Networks Text Classification
BITS Pilani, India · Independent Researcher
Evaluating Assurance Cases as Text-Attributed Graphs for Structure and Provenance Analysis
Apr 22, 2026Fariz Ikhwantri, Dusica MarijanAI Assurance AI-Generated Text Detection
Simula Research Laboratory Oslo, Norway
TRN-R1-Zero: Text-rich Network Reasoning via LLMs with Reinforcement Learning Only
Apr 21, 2026Yilun Liu, Ruihong Qiu, Zi HuangZero-Shot Learning LLM Reasoning with Graphs
School of Electrical Engineering and Computer Science The University of Queensland Brisbane, Queensland, Australia

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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