mlsanityData to 7 Oct 2026

Beyond Text: A Deep Dive into Large Language Models' Ability on Understanding Graph Data

arXiv 2310.04944 ↗First posted 7 Oct 2023cs.LGcited by 25

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Abstract (arXiv)

Large language models (LLMs) have achieved impressive performance on many natural language processing tasks. However, their capabilities on graph-structured data remain relatively unexplored. In this paper, we conduct a series of experiments benchmarking leading LLMs on diverse graph prediction tasks spanning node, edge, and graph levels. We aim to assess whether LLMs can effectively process graph data and leverage topological structures to enhance performance, compared to specialized graph neural networks. Through varied prompt formatting and task/dataset selection, we analyze how well LLMs can interpret and utilize graph structures. By comparing LLMs' performance with specialized graph models, we offer insights into the strengths and limitations of employing LLMs for graph analytics. Our findings provide insights into LLMs' capabilities and suggest avenues for further exploration in applying them to graph analytics.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). Builds on 32 of its 46 references that are in the map. First posted on arXiv 7 Oct 2023.

Builds on

32 of 46 references.

References · 32

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20 more
  1. 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,634
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 17; 2025: 4; 2026: 3.

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  1. 2025A Survey of Context Engineering for Large Language Modelscited by 120
  2. 2023A Survey of Graph Meets Large Language Model: Progress and Future Directionscited by 84
  3. 2024GRAG: Graph Retrieval-Augmented Generationcited by 77
  4. 2024A Survey of Large Language Models for Graphscited by 71
  5. 2024Demystifying Chains, Trees, and Graphs of Thoughtscited by 63
  6. 2024Graph Machine Learning in the Era of Large Language Models (LLMs)cited by 33
  7. 2024Distilling Large Language Models for Text-Attributed Graph Learningcited by 20
  8. 2024A Survey of Large Language Models on Generative Graph Analytics: Query, Learning, and Applicationscited by 17
  9. 2024Advancing Graph Representation Learning with Large Language Models: A Comprehensive Survey of Techniquescited by 16
  10. 2024CLEAR: Can Language Models Really Understand Causal Graphs?cited by 14
  11. 2024GP-GPT: Large Language Model for Gene-Phenotype Mappingcited by 10
  12. 2024A Survey on Self-Supervised Graph Foundation Models: Knowledge-Based Perspectivecited by 9
13 more
  1. 2024TAGA: Text-Attributed Graph Self-Supervised Learning by Synergizing Graph and Text Mutual Transformationscited by 5
  2. 2024Graph Linearization Methods for Reasoning on Graphs with Large Language Modelscited by 5
  3. 2025Large Language Models Meet Text-Attributed Graphs: A Survey of Integration Frameworks and Applicationscited by 5
  4. 2024Knowledge Graph Modeling-Driven Large Language Model Operating System (LLM OS) for Task Automation in Process Engineering Problem-Solvingcited by 4
  5. 2024From Anchors to Answers: A Novel Node Tokenizer for Integrating Graph Structure into Large Language Modelscited by 4
  6. 2024A Hierarchical Language Model For Interpretable Graph Reasoningcited by 3
  7. 2024Large Language Models Meet Graph Neural Networks: A Perspective of Graph Miningcited by 3
  8. 2025Large Language Models for Cryptocurrency Transaction Analysis: A Bitcoin Case Studycited by 3
  9. 2024Towards Graph Prompt Learning: A Survey and Beyondcited by 1
  10. 2026Beyond One-Size-Fits-All: Adaptive Subgraph Denoising for Zero-Shot Graph Learning with Large Language Modelscited by 1
  11. 2026Riemannian Geometry Speaks Louder Than Words: From Graph Foundation Model to Next-Generation Graph Intelligencecited by 1
  12. 2025Towards Multimodal Graph Large Language Modelcited by 0
  13. 2026Are Large Language Models Suitable for Graph Computation? Progress and Prospectscited by 0