mlsanityData to 7 Oct 2026

GraphInstruct: Empowering Large Language Models with Graph Understanding and Reasoning Capability

arXiv 2403.04483 ↗First posted 7 Mar 2024cs.AI cs.CLcited by 57

On the map: LLM reasoning → Knowledge- and tool-augmented reasoning

Abstract (arXiv)

Improving the general capabilities of large language models (LLMs) is an active research topic. As a common data structure in many real-world domains, understanding graph data is a crucial part of advancing general intelligence. To this end, we propose a dynamic benchmark named GraphInstruct in this paper, which comprehensively includes 21 classical graph reasoning tasks, providing diverse graph generation pipelines and detailed intermediate reasoning steps for each sample. Based on GraphInstruct, we develop GraphSolver via efficient instruction-tuning, which demonstrates prominent graph understanding capability compared to other open-sourced LLMs. To further endow LLMs with multi-step graph reasoning capability, we propose a label-mask training strategy and build GraphSolver+, which leverages masked supervision on intermediate reasoning tokens to emphasize crucial node-identification signals. As one of the pioneering efforts to enhance the graph understanding and reasoning abilities of LLMs, extensive experiments have demonstrated the superiority of GraphSolver and GraphSolver+ over other LLMs. We sincerely hope GraphInstruct will facilitate further research on applying LLMs to graph-structured data. Our code and data are released publicly at: https://github.com/CGCL-codes/GraphInstruct.

Key facts. Cited by 57 papers in the map (data through 7 Oct 2026). Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning. Builds on 15 of its 27 references that are in the map. First posted on arXiv 7 Mar 2024.

Builds on

15 of 27 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Can Language Models Solve Graph Problems in Natural Language?17 May

Other references · 14

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2022Training language models to follow instructions with human feedbackcited by 15,712
  3. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  4. 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
  5. 2023BloombergGPT: A Large Language Model for Financecited by 923
  6. 2023WizardCoder: Empowering Code Large Language Models with Evol-Instructcited by 728
  7. 2023TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendationcited by 336
  8. 2023ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledgecited by 324
  9. 2023Exploring the Potential of Large Language Models (LLMs) in Learning on Graphscited by 271
  10. 2023AudioGPT: Understanding and Generating Speech, Music, Sound, and Talking Headcited by 264
  11. 2023GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarkingcited by 209
  12. 2023Chatlaw: A Multi-Agent Legal Assistant based on a Role-Aligned Mixture-of-Experts Architecturecited by 119
2 more
  1. 2023GPT Can Solve Mathematical Problems Without a Calculatorcited by 68
  2. 2023EcomGPT: Instruction-tuning Large Language Models with Chain-of-Task Tasks for E-commercecited by 48

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 20; 2025: 22; 2026: 15.

57 papers.

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  1. 2024Retrieval-Augmented Generation with Graphs (GraphRAG)cited by 170
  2. 2025Reasoning by Superposition: A Theoretical Perspective on Chain of Continuous Thoughtcited by 73
  3. 2024A Survey of Large Language Models for Graphscited by 71
  4. 2024GraphArena: Evaluating and Exploring Large Language Models on Graph Computationcited by 48
  5. 2024Can LLM Graph Reasoning Generalize beyond Pattern Memorization?cited by 46
  6. 2024Can Graph Learning Improve Planning in LLM-based Agents?cited by 42
  7. 2024How Do Large Language Models Understand Graph Patterns? A Benchmark for Graph Pattern Comprehensioncited by 30
  8. 2025Graphs Meet AI Agents: Taxonomy, Progress, and Future Opportunitiescited by 23
  9. 2024ALPINE: Unveiling the Planning Capability of Autoregressive Learning in Language Modelscited by 18
  10. 2024GraphInsight: Unlocking Insights in Large Language Models for Graph Structure Understandingcited by 18
  11. 2025RTL++: Graph-enhanced LLM for RTL Code Generationcited by 16
  12. 2024CLEAR: Can Language Models Really Understand Causal Graphs?cited by 14
38 more
  1. 2024Can MLLMs Understand the Deep Implication Behind Chinese Images?cited by 14
  2. 2025G1: Teaching LLMs to Reason on Graphs with Reinforcement Learningcited by 14
  3. 2024GraCoRe: Benchmarking Graph Comprehension and Complex Reasoning in Large Language Modelscited by 13
  4. 2024GraphTeam: Facilitating Large Language Model-based Graph Analysis via Multi-Agent Collaborationcited by 12
  5. 2025HiBench: Benchmarking LLMs Capability on Hierarchical Structure Reasoningcited by 12
  6. 2024Beyond Graphs: Can Large Language Models Comprehend Hypergraphs?cited by 11
  7. 2024GCoder: Improving Large Language Model for Generalized Graph Problem Solvingcited by 11
  8. 2025A Survey of Large Language Model-Powered Spatial Intelligence Across Scales: Advances in Embodied Agents, Smart Cities, and Earth Sciencecited by 11
  9. 2024Revisiting the Graph Reasoning Ability of Large Language Models: Case Studies in Translation, Connectivity and Shortest Pathcited by 10
  10. 2025Rewarding Graph Reasoning Process makes LLMs more Generalized Reasonerscited by 10
  11. 2024A Survey on Self-Supervised Graph Foundation Models: Knowledge-Based Perspectivecited by 9
  12. 2025GraphThought: Graph Combinatorial Optimization with Thought Generationcited by 9
  13. 2025GraphCogent: Mitigating LLMs' Working Memory Constraints via Multi-Agent Collaboration in Complex Graph Understandingcited by 9
  14. 2025Understanding and Enhancing the Planning Capability of Language Models via Multi-Token Predictioncited by 6
  15. 2025Rethinking and Benchmarking Large Language Models for Graph Reasoningcited by 6
  16. 2025Lost in Serialization: Invariance and Generalization of LLM Graph Reasonerscited by 6
  17. 2024Investigating Instruction Tuning Large Language Models on Graphscited by 5
  18. 2024GUNDAM: Aligning Large Language Models with Graph Understandingcited by 5
  19. 2024Graph Linearization Methods for Reasoning on Graphs with Large Language Modelscited by 5
  20. 2025Benefits and Pitfalls of Reinforcement Learning for Language Model Planning: A Theoretical Perspectivecited by 4
  21. 2025Chain of Execution Supervision Promotes General Reasoning in Large Language Modelscited by 4
  22. 2026GraphSkill: Documentation-Guided Hierarchical Retrieval-Augmented Coding for Complex Graph Reasoningcited by 4
  23. 2024A Hierarchical Language Model For Interpretable Graph Reasoningcited by 3
  24. 2025Language Models, Graph Searching, and Supervision Adulteration: When More Supervision is Less and How to Make More Morecited by 3
  25. 2025KG-HTC: Integrating Knowledge Graphs into LLMs for Effective Zero-shot Hierarchical Text Classificationcited by 3
  26. 2026Exposing Weaknesses of Large Reasoning Models through Graph Algorithm Problemscited by 3
  27. 2025Graph Drawing for LLMs: An Empirical Evaluationcited by 2
  28. 2025See or Say Graphs: Agent-Driven Scalable Graph Structure Understanding with Vision-Language Modelscited by 2
  29. 2026Construct, Align, and Reason: Large Ontology Models for Enterprise Knowledge Managementcited by 2
  30. 2025Improving LLMs' Generalized Reasoning Abilities by Graph Problemscited by 1
  31. 2025H4G: Unlocking Faithful Inference for Zero-Shot Graph Learning in Hyperbolic Spacecited by 1
  32. 2026GraphScout: Empowering Large Language Models with Intrinsic Exploration Ability for Agentic Graph Reasoningcited by 1
  33. 2026Clustering as Reasoning: A k-Means Interpretation of Chain-of-Thought Graph Learningcited by 1
  34. 2025GraphShaper: Geometry-aware Alignment for Improving Transfer Learning in Text-Attributed Graphscited by 0
  35. 2025Efficiently Learning Branching Networks for Multitask Algorithmic Reasoningcited by 0
  36. 2026From Perception to Action: Spatial AI Agents and World Modelscited by 0
  37. 2026Are Large Language Models Suitable for Graph Computation? Progress and Prospectscited by 0
  38. 2026GraphInfer-Bench: Benchmarking LLM's Inference Capability on Graphscited by 0

50 most cited of 57 All 57 (JSON)

Similar, not cited

Close in wording, no citation either way

  1. 2025Exploring Graph Learning Tasks with Pure LLMs: A Comprehensive Benchmark and Investigation
  2. 2023Talk like a Graph: Encoding Graphs for Large Language Models
  3. 2023GraphLLM: Boosting Graph Reasoning Ability of Large Language Model
  4. 2024GraphEval36K: Benchmarking Coding and Reasoning Capabilities of Large Language Models on Graph Datasets
  5. 2024InstructGraph: Boosting Large Language Models via Graph-centric Instruction Tuning and Preference Alignment
  6. 2024GraphWiz: An Instruction-Following Language Model for Graph Problems
  7. 2026Teaching LLMs to See Graphs: Unifying Text and Structural Reasoning
  8. 2026Unified Multi-Dimensional Benchmark for Complex Graph Reasoning in Large Language Models