mlsanityData to 14 Oct 2026

GraphLLM: Boosting Graph Reasoning Ability of Large Language Model

arXiv 2310.05845 ↗First posted 9 Oct 2023cs.CL cs.AIcited by 107

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

Abstract (arXiv)

The advancement of Large Language Models (LLMs) has remarkably pushed the boundaries towards artificial general intelligence (AGI), with their exceptional ability on understanding diverse types of information, including but not limited to images and audio. Despite this progress, a critical gap remains in empowering LLMs to proficiently understand and reason on graph data. Recent studies underscore LLMs' underwhelming performance on fundamental graph reasoning tasks. In this paper, we endeavor to unearth the obstacles that impede LLMs in graph reasoning, pinpointing the common practice of converting graphs into natural language descriptions (Graph2Text) as a fundamental bottleneck. To overcome this impediment, we introduce GraphLLM, a pioneering end-to-end approach that synergistically integrates graph learning models with LLMs. This synergy equips LLMs with the ability to proficiently interpret and reason on graph data, harnessing the superior expressive power of graph learning models. Our empirical evaluations across four fundamental graph reasoning tasks validate the effectiveness of GraphLLM. The results exhibit a substantial average accuracy enhancement of 54.44%, alongside a noteworthy context reduction of 96.45% across various graph reasoning tasks.

Key facts. Cited by 107 papers in the map (data through 14 Oct 2026). Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning. Builds on 28 of its 57 references that are in the map. First posted on arXiv 9 Oct 2023.

Builds on

28 of 57 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023Can Language Models Solve Graph Problems in Natural Language?17 May
  2. 2023Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Augmented by ChatGPT10 Apr

Other references · 26

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2023GPT-4 Technical Reportcited by 18,396
  3. 2022Training language models to follow instructions with human feedbackcited by 15,971
  4. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  5. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  6. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  7. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  8. 2017Graph Attention Networkscited by 8,923
  9. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,326
  10. 2018How Powerful are Graph Neural Networks?cited by 4,393
  11. 2022Flamingo: a Visual Language Model for Few-Shot Learningcited by 4,386
  12. 2019Parameter-Efficient Transfer Learning for NLPcited by 4,236
14 more
  1. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,915
  2. 2022FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awarenesscited by 3,673
  3. 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,512
  4. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,455
  5. 2023mPLUG-Owl: Modularization Empowers Large Language Models with Multimodalitycited by 1,040
  6. 2023A Survey on Multimodal Large Language Modelscited by 909
  7. 2023LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Modelcited by 602
  8. 2023VisionLLM: Large Language Model is also an Open-Ended Decoder for Vision-Centric Taskscited by 501
  9. 2023PointLLM: Empowering Large Language Models to Understand Point Cloudscited by 333
  10. 2023AudioGPT: Understanding and Generating Speech, Music, Sound, and Talking Headcited by 265
  11. 2023Language is All a Graph Needscited by 220
  12. 2023GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarkingcited by 210
  13. 2023Graph Inductive Biases in Transformers without Message Passingcited by 153
  14. 2023What Matters in Training a GPT4-Style Language Model with Multimodal Inputs?cited by 64

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 5; 2024: 44; 2025: 36; 2026: 22.

107 papers.

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  1. 2024G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answeringcited by 223
  2. 2023Large Language Models on Graphs: A Comprehensive Surveycited by 192
  3. 2024Retrieval-Augmented Generation with Graphs (GraphRAG)cited by 172
  4. 2024Position: Graph Foundation Models are Already Herecited by 86
  5. 2023A Survey of Graph Meets Large Language Model: Progress and Future Directionscited by 85
  6. 2024GFT: Graph Foundation Model with Transferable Tree Vocabularycited by 85
  7. 2024OpenGraph: Towards Open Graph Foundation Modelscited by 78
  8. 2024Understanding Transformer Reasoning Capabilities via Graph Algorithmscited by 67
  9. 2024Demystifying Chains, Trees, and Graphs of Thoughtscited by 63
  10. 2024KG-RAG: Bridging the Gap Between Knowledge and Creativitycited by 56
  11. 2024GraphArena: Evaluating and Exploring Large Language Models on Graph Computationcited by 49
  12. 2024HiGPT: Heterogeneous Graph Language Modelcited by 45
38 more
  1. 2024Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insightscited by 44
  2. 2024Can Graph Learning Improve Planning in LLM-based Agents?cited by 43
  3. 2024Fully-inductive Node Classification on Arbitrary Graphscited by 39
  4. 2024Graph Machine Learning in the Era of Large Language Models (LLMs)cited by 33
  5. 2024GraphWiz: An Instruction-Following Language Model for Graph Problemscited by 32
  6. 2024How Do Large Language Models Understand Graph Patterns? A Benchmark for Graph Pattern Comprehensioncited by 30
  7. 2023Large Language Models as Topological Structure Enhancers for Text-Attributed Graphscited by 27
  8. 2024Entity Alignment with Noisy Annotations from Large Language Modelscited by 22
  9. 2025LLM as GNN: Graph Vocabulary Learning for Text-Attributed Graph Foundation Modelscited by 22
  10. 2024Scalable and Accurate Graph Reasoning with LLM-based Multi-Agentscited by 21
  11. 2025When Do LLMs Help With Node Classification? A Comprehensive Analysiscited by 21
  12. 2024ALPINE: Unveiling the Planning Capability of Autoregressive Learning in Language Modelscited by 18
  13. 2025RTL++: Graph-enhanced LLM for RTL Code Generationcited by 18
  14. 2024A Survey of Large Language Models on Generative Graph Analytics: Query, Learning, and Applicationscited by 17
  15. 2024LLM and GNN are Complementary: Distilling LLM for Multimodal Graph Learningcited by 17
  16. 2024Can Large Language Models Analyze Graphs like Professionals? A Benchmark, Datasets and Modelscited by 17
  17. 2026Graph is a Substrate Across Data Modalitiescited by 14
  18. 2024GraphEval36K: Benchmarking Coding and Reasoning Capabilities of Large Language Models on Graph Datasetscited by 12
  19. 2024GraphTeam: Facilitating Large Language Model-based Graph Analysis via Multi-Agent Collaborationcited by 12
  20. 2024Can LLMs Understand Computer Networks? Towards a Virtual System Administratorcited by 11
  21. 2024Learning on Graphs with Large Language Models(LLMs): A Deep Dive into Model Robustnesscited by 11
  22. 2024Struct-X: Enhancing Large Language Models Reasoning with Structured Datacited by 11
  23. 2024GP-GPT: Large Language Model for Gene-Phenotype Mappingcited by 11
  24. 2024Revisiting the Graph Reasoning Ability of Large Language Models: Case Studies in Translation, Connectivity and Shortest Pathcited by 10
  25. 2024Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Taskscited by 10
  26. 2025Can TabPFN Compete with GNNs for Node Classification via Graph Tabularization?cited by 10
  27. 2026Learning Posterior Predictive Distributions for Node Classification from Synthetic Graph Priorscited by 10
  28. 2024Can LLMs perform structured graph reasoning?cited by 9
  29. 2024A Survey on Self-Supervised Graph Foundation Models: Knowledge-Based Perspectivecited by 9
  30. 2025GraphThought: Graph Combinatorial Optimization with Thought Generationcited by 9
  31. 2023Graph Agent: Explicit Reasoning Agent for Graphscited by 8
  32. 2024Source Code Foundation Models are Transferable Binary Analysis Knowledge Basescited by 8
  33. 2025Graph-Augmented Reasoning: Evolving Step-by-Step Knowledge Graph Retrieval for LLM Reasoningcited by 8
  34. 2025Grounding LLM Reasoning with Knowledge Graphscited by 7
  35. 2025A Comprehensive Data-centric Overview of Federated Graph Learningcited by 7
  36. 2025GRAIL:Learning to Interact with Large Knowledge Graphs for Retrieval Augmented Reasoningcited by 7
  37. 2024Thinking with Knowledge Graphs: Enhancing LLM Reasoning Through Structured Datacited by 6
  38. 2025Bridging Visualization and Optimization: Multimodal Large Language Models on Graph-Structured Combinatorial Optimizationcited by 6

50 most cited of 107 All 107 (JSON)

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  1. 2025Exploring Graph Learning Tasks with Pure LLMs: A Comprehensive Benchmark and Investigation
  2. 2024GraphInstruct: Empowering Large Language Models with Graph Understanding and Reasoning Capability
  3. 2023Evaluating Large Language Models on Graphs: Performance Insights and Comparative Analysis
  4. 2024Can LLM Graph Reasoning Generalize beyond Pattern Memorization?
  5. 2023GraphText: Graph Reasoning in Text Space
  6. 2023Talk like a Graph: Encoding Graphs for Large Language Models
  7. 2026Are Large Language Models Suitable for Graph Computation? Progress and Prospects
  8. 2026LoReC: Rethinking Large Language Models for Graph Data Analysis