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.

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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
Hypergraph as Language
May 21, 2026Mengqi Lei, Guohuan Xie, Shihui Ying +4LLM Alignment Hypergraph Learning
1{BNRist, THUIBCS, BLBCI, School of Software}, Tsinghua University · 2Yangtze Delta Region Institute, Tsinghua University · 3Shanghai Institute of Applied Mathematics and Mechanics, Shanghai University +1
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
SLASH the Sink: Sharpening Structural Attention Inside LLMs
May 11, 2026Yiming Liu, Bin Lu, Xinbing Wang +2LLM Reasoning with Graphs LLM Interpretability
Shanghai Jiao Tong University, Shanghai, China · Institute of Geographical Science and Natural Resources Research, Chinese Academy of Sciences, Beijing, 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
When Graph Language Models Go Beyond Memorization
May 7, 2026Masatsugu Yamada, Mahito SugiyamaGraph Generation Memorization in Language Models
National Institute of Informatics Tokyo, Japan · National Institute of Informatics, Tokyo, Japan · SOKENDAI, Kanagawa, Japan
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding
May 5, 2026Zhongjian Zhang, Yue Yu, Mengmei Zhang +3LLM Evaluation LLM Reasoning with Graphs
Beijing University of Posts and Telecommunications · China Telecom Bestpay · Beihang University
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
LoReC: Rethinking Large Language Models for Graph Data Analysis
Apr 20, 2026Hongyu Zhan, Qixin Wang, Yusen Tan +6Graph Representation Learning LLMs for Graph Learning
The Hong Kong University of Science and Technology (Guangzhou) · Tongji University · Westlake University +2
HopRank: Self-Supervised LLM Preference-Tuning on Graphs for Few-Shot Node Classification
Apr 19, 2026Ziqing Wang, Kaize DingLLM Alignment Graph SSL
Northwestern University
LLM as GNN: Graph Vocabulary Learning for Text-Attributed Graph Foundation Models
Mar 5, 2025Xi Zhu, Haochen Xue, Ziwei Zhao +7Text-Attributed Graph Learning Graph Foundation Models
1Rutgers University · 2Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) · University of Science and Technology of China +2
Large Language Models for Cryptocurrency Transaction Analysis: A Bitcoin Case Study
Jan 30, 2025Yuchen Lei, Yuexin Xiang, Rafael Dowsley +5LLM Evaluation LLMs for Cybersecurity
School of Cyber Science and Engineering, Wuhan University, Wuhan 430072, China · Faculty of Information Technology, Monash University, Clayton, VIC 3800, Australia · CSIRO’s Data61, Eveleigh, NSW 2015, Australia +2

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Clustering as Reasoning: A $k$ -Means Interpretation of Chain-of-Thought Graph Learning

Xuanting Xie, Zhaochen Guo, Bingheng Li, Xingtong Yu+3

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

Graph Representation Learning Text-Attributed Graph Learning LLMs for Graph Learning

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Chain-of-Thought (CoT) prompting has shown promise in enhancing the reasoning capabilities of large language models (LLMs) on text-attributed graphs (TAGs). This work reframes CoT-based graph learning through the principle of clustering as reasoning, offering a

k

-means interpretation of how iterative reasoning operates over graph-structured data. We observe that existing graph CoT methods rely on disjoint architectures and fixed graph representations, limiting step-by-step semantic-topological interaction and interpretability. To overcome this limitation, we propose a unified framework named KCoT that integrates CoT reasoning with graph representation learning. Our key theoretical result reveals a formal mathematical correspondence between a Transformer block and the

k

-means algorithm, allowing reasoning to be interpreted as iterative assignment and update steps. Based on this insight, we introduce a Semantic Discriminating Prompt that explicitly formulates these steps as structured CoT reasoning, together with a structure-grounded alignment strategy to fuse topological priors with evolving thought-conditioned representations. Experiments on standard benchmarks demonstrate consistent improvements over state-of-the-art methods, validating clustering as a principled mechanism for CoT-based graph learning.

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