mlsanityData to 14 Oct 2026

GraphText: Graph Reasoning in Text Space

arXiv 2310.01089 ↗First posted 2 Oct 2023cs.CL cs.LGcited by 111

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

Abstract (arXiv)

Large Language Models (LLMs) have gained the ability to assimilate human knowledge and facilitate natural language interactions with both humans and other LLMs. However, despite their impressive achievements, LLMs have not made significant advancements in the realm of graph machine learning. This limitation arises because graphs encapsulate distinct relational data, making it challenging to transform them into natural language that LLMs understand. In this paper, we bridge this gap with a novel framework, GraphText, that translates graphs into natural language. GraphText derives a graph-syntax tree for each graph that encapsulates both the node attributes and inter-node relationships. Traversal of the tree yields a graph text sequence, which is then processed by an LLM to treat graph tasks as text generation tasks. Notably, GraphText offers multiple advantages. It introduces training-free graph reasoning: even without training on graph data, GraphText with ChatGPT can achieve on par with, or even surpassing, the performance of supervised-trained graph neural networks through in-context learning (ICL). Furthermore, GraphText paves the way for interactive graph reasoning, allowing both humans and LLMs to communicate with the model seamlessly using natural language. These capabilities underscore the vast, yet-to-be-explored potential of LLMs in the domain of graph machine learning.

Key facts. Cited by 111 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 46 of its 58 references that are in the map. First posted on arXiv 2 Oct 2023.

Builds on

46 of 58 references.

Closest ancestors · 1

Same direction, newest first

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

Other references · 45

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  3. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  4. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  5. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  6. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  7. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  8. 2017Graph Attention Networkscited by 8,923
  9. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  10. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  11. 2017Inductive Representation Learning on Large Graphscited by 6,320
  12. 2018How Powerful are Graph Neural Networks?cited by 4,393
33 more
  1. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  2. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,568
  3. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,211
  4. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,592
  5. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,234
  6. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Frameworkcited by 1,808
  7. 2019Simplifying Graph Convolutional Networkscited by 1,477
  8. 2022Code as Policies: Language Model Programs for Embodied Controlcited by 1,210
  9. 2023PaLM 2 Technical Reportcited by 1,162
  10. 2018Predict then Propagate: Graph Neural Networks meet Personalized PageRankcited by 993
  11. 2018Representation Learning on Graphs with Jumping Knowledge Networkscited by 877
  12. 2020Simple and Deep Graph Convolutional Networkscited by 860
  13. 2021How Attentive are Graph Attention Networks?cited by 835
  14. 2020Geom-GCN: Geometric Graph Convolutional Networkscited by 826
  15. 2020Adaptive Universal Generalized PageRank Graph Neural Networkcited by 595
  16. 2023VisionLLM: Large Language Model is also an Open-Ended Decoder for Vision-Centric Taskscited by 501
  17. 2021Rethinking Graph Transformers with Spectral Attentioncited by 473
  18. 2022Complexity-Based Prompting for Multi-Step Reasoningcited by 388
  19. 2021Graph Neural Networks with Learnable Structural and Positional Representationscited by 333
  20. 2021Do Transformers Really Perform Bad for Graph Representation?cited by 320
  21. 2023Exploring the Potential of Large Language Models (LLMs) in Learning on Graphscited by 274
  22. 2019Learning Discrete Structures for Graph Neural Networkscited by 231
  23. 2023Language is All a Graph Needscited by 220
  24. 2023GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarkingcited by 210
  25. 2020Data Augmentation for Graph Neural Networkscited by 203
  26. 2022Leveraging Large Language Models for Multiple Choice Question Answeringcited by 203
  27. 2020Scaling Graph Neural Networks with Approximate PageRankcited by 174
  28. 2022Learning on Large-scale Text-attributed Graphs via Variational Inferencecited by 155
  29. 2021Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Predictioncited by 138
  30. 2022Language Model Cascadescited by 107
  31. 2022Decoupling the Depth and Scope of Graph Neural Networkscited by 94
  32. 2022Graph Attention Multi-Layer Perceptroncited by 83
  33. 2021AdsGNN: Behavior-Graph Augmented Relevance Modeling in Sponsored Searchcited by 38

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 5; 2024: 46; 2025: 37; 2026: 23.

111 papers.

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  1. 2024Graph Retrieval-Augmented Generation: A Surveycited by 324
  2. 2024G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answeringcited by 223
  3. 2023Large Language Models on Graphs: A Comprehensive Surveycited by 192
  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. 2024ZeroG: Investigating Cross-dataset Zero-shot Transferability in Graphscited by 85
  7. 2024GFT: Graph Foundation Model with Transferable Tree Vocabularycited by 85
  8. 2023Label-free Node Classification on Graphs with Large Language Models (LLMS)cited by 72
  9. 2024A Survey of Large Language Models for Graphscited by 72
  10. 2024Demystifying Chains, Trees, and Graphs of Thoughtscited by 63
  11. 2024GraphCLIP: Enhancing Transferability in Graph Foundation Models for Text-Attributed Graphscited by 58
  12. 2024InstructGraph: Boosting Large Language Models via Graph-centric Instruction Tuning and Preference Alignmentcited by 56
38 more
  1. 2024Can LLM Graph Reasoning Generalize beyond Pattern Memorization?cited by 47
  2. 2025Griffin: Towards a Graph-Centric Relational Database Foundation Modelcited by 47
  3. 2024Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insightscited by 44
  4. 2024Fully-inductive Node Classification on Arbitrary Graphscited by 39
  5. 2024GLBench: A Comprehensive Benchmark for Graph with Large Language Modelscited by 36
  6. 2024Graph Machine Learning in the Era of Large Language Models (LLMs)cited by 33
  7. 2024LLMs generate structurally realistic social networks but overestimate political homophilycited by 33
  8. 2024GraphWiz: An Instruction-Following Language Model for Graph Problemscited by 32
  9. 2024How Do Large Language Models Understand Graph Patterns? A Benchmark for Graph Pattern Comprehensioncited by 30
  10. 2024DTGB: A Comprehensive Benchmark for Dynamic Text-Attributed Graphscited by 27
  11. 2025G-Refer: Graph Retrieval-Augmented Large Language Model for Explainable Recommendationcited by 27
  12. 2024Entity Alignment with Noisy Annotations from Large Language Modelscited by 22
  13. 2025LLM as GNN: Graph Vocabulary Learning for Text-Attributed Graph Foundation Modelscited by 22
  14. 2024ProtLLM: An Interleaved Protein-Language LLM with Protein-as-Word Pre-Trainingcited by 21
  15. 2025GraphICL: Unlocking Graph Learning Potential in LLMs through Structured Prompt Designcited by 21
  16. 2025When Do LLMs Help With Node Classification? A Comprehensive Analysiscited by 21
  17. 2025Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Surveycited by 18
  18. 2024A Survey of Large Language Models on Generative Graph Analytics: Query, Learning, and Applicationscited by 17
  19. 2024Advancing Graph Representation Learning with Large Language Models: A Comprehensive Survey of Techniquescited by 16
  20. 2024Can Graph Descriptive Order Affect Solving Graph Problems with LLMs?cited by 16
  21. 2024Large Language Model-driven Meta-structure Discovery in Heterogeneous Information Networkcited by 16
  22. 2024Revisiting Random Walks for Learning on Graphscited by 13
  23. 2024GraphTeam: Facilitating Large Language Model-based Graph Analysis via Multi-Agent Collaborationcited by 12
  24. 2025Exploring Graph Learning Tasks with Pure LLMs: A Comprehensive Benchmark and Investigationcited by 12
  25. 2025MLaGA: Multimodal Large Language and Graph Assistantcited by 12
  26. 2023LLMs as Visual Explainers: Advancing Image Classification with Evolving Visual Descriptionscited by 11
  27. 2024Node Identifiers: Compact, Discrete Representations for Efficient Graph Learningcited by 10
  28. 2024Revisiting the Graph Reasoning Ability of Large Language Models: Case Studies in Translation, Connectivity and Shortest Pathcited by 10
  29. 2024Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Taskscited by 10
  30. 2025Contextualizing biological perturbation experiments through languagecited by 10
  31. 2025Can TabPFN Compete with GNNs for Node Classification via Graph Tabularization?cited by 10
  32. 2026Learning Posterior Predictive Distributions for Node Classification from Synthetic Graph Priorscited by 10
  33. 2024A Survey on Self-Supervised Graph Foundation Models: Knowledge-Based Perspectivecited by 9
  34. 2025Controllable 3D Outdoor Scene Generation via Scene Graphscited by 9
  35. 2023Graph Agent: Explicit Reasoning Agent for Graphscited by 8
  36. 2024LOGIN: A Large Language Model Consulted Graph Neural Network Training Frameworkcited by 8
  37. 2024LangTopo: Aligning Language Descriptions of Graphs with Tokenized Topological Modelingcited by 7
  38. 2024Enhance Graph Alignment for Large Language Modelscited by 7

50 most cited of 111 All 111 (JSON)

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  1. 2023GraphLLM: Boosting Graph Reasoning Ability of Large Language Model
  2. 2024GUNDAM: Aligning Large Language Models with Graph Understanding
  3. 2023Talk like a Graph: Encoding Graphs for Large Language Models
  4. 2023Evaluating Large Language Models on Graphs: Performance Insights and Comparative Analysis
  5. 2024GraphInstruct: Empowering Large Language Models with Graph Understanding and Reasoning Capability
  6. 2024Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs
  7. 2025GraphOmni: A Comprehensive and Extensible Benchmark Framework for Large Language Models on Graph-theoretic Tasks
  8. 2025GRIP: In-Parameter Graph Reasoning through Fine-Tuning Large Language Models