mlsanityData to 7 Oct 2026

GraphGPT: Graph Instruction Tuning for Large Language Models

arXiv 2310.13023 ↗First posted 19 Oct 2023cs.CL cs.AIcited by 223

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Cited by 21 in LLM reasoning, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Graph Neural Networks (GNNs) have evolved to understand graph structures through recursive exchanges and aggregations among nodes. To enhance robustness, self-supervised learning (SSL) has become a vital tool for data augmentation. Traditional methods often depend on fine-tuning with task-specific labels, limiting their effectiveness when labeled data is scarce. Our research tackles this by advancing graph model generalization in zero-shot learning environments. Inspired by the success of large language models (LLMs), we aim to create a graph-oriented LLM capable of exceptional generalization across various datasets and tasks without relying on downstream graph data. We introduce the GraphGPT framework, which integrates LLMs with graph structural knowledge through graph instruction tuning. This framework includes a text-graph grounding component to link textual and graph structures and a dual-stage instruction tuning approach with a lightweight graph-text alignment projector. These innovations allow LLMs to comprehend complex graph structures and enhance adaptability across diverse datasets and tasks. Our framework demonstrates superior generalization in both supervised and zero-shot graph learning tasks, surpassing existing benchmarks. The open-sourced model implementation of our GraphGPT is available at https://github.com/HKUDS/GraphGPT.

Key facts. Cited by 223 papers in the map (data through 7 Oct 2026). Builds on 46 of its 62 references that are in the map. First posted on arXiv 19 Oct 2023.

Builds on

46 of 62 references.

References · 46

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  4. 2022Training language models to follow instructions with human feedbackcited by 15,712
  5. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  6. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  7. 2017Graph Attention Networkscited by 8,854
  8. 2017Inductive Representation Learning on Large Graphscited by 6,284
  9. 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
  10. 2022Constitutional AI: Harmlessness from AI Feedbackcited by 2,950
  11. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,674
  12. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,148
34 more
  1. 2022Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?cited by 1,413
  2. 2020Graph Contrastive Learning with Augmentationscited by 1,203
  3. 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,134
  4. 2018Deep Graph Infomaxcited by 1,101
  5. 2022GLM-130B: An Open Bilingual Pre-trained Modelcited by 837
  6. 2019Heterogeneous Graph Attention Networkcited by 732
  7. 2023Baichuan 2: Open Large-scale Language Modelscited by 664
  8. 2020Heterogeneous Graph Transformercited by 581
  9. 2019Graph Transformer Networkscited by 520
  10. 2020Graph Contrastive Learning with Adaptive Augmentationcited by 519
  11. 2022GraphMAE: Self-Supervised Masked Graph Autoencoderscited by 443
  12. 2023How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resourcescited by 405
  13. 2020GPT-GNN: Generative Pre-Training of Graph Neural Networkscited by 302
  14. 2021Graph Self-Supervised Learning: A Surveycited by 289
  15. 2021Graph Contrastive Learning Automatedcited by 287
  16. 2023Exploring the Potential of Large Language Models (LLMs) in Learning on Graphscited by 271
  17. 2023NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classificationcited by 207
  18. 2022SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentationcited by 171
  19. 2021Training Graph Neural Networks with 1000 Layerscited by 169
  20. 2023GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networkscited by 166
  21. 2022Distilling Reasoning Capabilities into Smaller Language Modelscited by 155
  22. 2021Graph-less Neural Networks: Teaching Old MLPs New Tricks via Distillationcited by 151
  23. 2022Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecastingcited by 117
  24. 2021Learning Intents behind Interactions with Knowledge Graph for Recommendationcited by 94
  25. 2023DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusioncited by 85
  26. 2021HDMI: High-order Deep Multiplex Infomaxcited by 82
  27. 2021AutoGCL: Automated Graph Contrastive Learning via Learnable View Generatorscited by 73
  28. 2023Augmenting Low-Resource Text Classification with Graph-Grounded Pre-training and Promptingcited by 62
  29. 2023Automated Self-Supervised Learning for Recommendationcited by 37
  30. 2021Graph Communal Contrastive Learningcited by 34
  31. 2023Adaptive Graph Contrastive Learning for Recommendationcited by 31
  32. 2022Dual Space Graph Contrastive Learningcited by 20
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  34. 2022Meta-Weight Graph Neural Network: Push the Limits Beyond Global Homophilycited by 14

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 10; 2024: 64; 2025: 72; 2026: 77.

223 papers.

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  1. 2024LightRAG: Simple and Fast Retrieval-Augmented Generationcited by 310
  2. 2024G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answeringcited by 217
  3. 2023Video Understanding with Large Language Models: A Surveycited by 203
  4. 2023Large Language Models on Graphs: A Comprehensive Surveycited by 190
  5. 2024LLaGA: Large Language and Graph Assistantcited by 177
  6. 2024Chain of Ideas: Revolutionizing Research Via Novel Idea Development with LLM Agentscited by 94
  7. 2023InstructMol: Multi-Modal Integration for Building a Versatile and Reliable Molecular Assistant in Drug Discoverycited by 86
  8. 2025A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Modelscited by 85
  9. 2023A Survey of Graph Meets Large Language Model: Progress and Future Directionscited by 84
  10. 2024Position: Graph Foundation Models are Already Herecited by 84
  11. 2024GFT: Graph Foundation Model with Transferable Tree Vocabularycited by 82
  12. 2024UrbanGPT: Spatio-Temporal Large Language Modelscited by 81
38 more
  1. 2024Plan-on-Graph: Self-Correcting Adaptive Planning of Large Language Model on Knowledge Graphscited by 77
  2. 2024OpenGraph: Towards Open Graph Foundation Modelscited by 75
  3. 2024StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurizationcited by 70
  4. 2024Demystifying Chains, Trees, and Graphs of Thoughtscited by 63
  5. 2024LLMs as Zero-shot Graph Learners: Alignment of GNN Representations with LLM Token Embeddingscited by 60
  6. 2024GraphCLIP: Enhancing Transferability in Graph Foundation Models for Text-Attributed Graphscited by 58
  7. 2024AnyGraph: Graph Foundation Model in the Wildcited by 51
  8. 2024Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insightscited by 44
  9. 2024Can Graph Learning Improve Planning in LLM-based Agents?cited by 42
  10. 2025Griffin: Towards a Graph-Centric Relational Database Foundation Modelcited by 41
  11. 2023LLM4EDA: Emerging Progress in Large Language Models for Electronic Design Automationcited by 40
  12. 2025GFM-RAG: Graph Foundation Model for Retrieval Augmented Generationcited by 40
  13. 2023Open-TI: Open Traffic Intelligence with Augmented Language Modelcited by 38
  14. 2024Fully-inductive Node Classification on Arbitrary Graphscited by 36
  15. 2024GLBench: A Comprehensive Benchmark for Graph with Large Language Modelscited by 36
  16. 2024Graph Machine Learning in the Era of Large Language Models (LLMs)cited by 33
  17. 2024GraphWiz: An Instruction-Following Language Model for Graph Problemscited by 32
  18. 2024GITA: Graph to Visual and Textual Integration for Vision-Language Graph Reasoningcited by 31
  19. 2024How Do Large Language Models Understand Graph Patterns? A Benchmark for Graph Pattern Comprehensioncited by 30
  20. 2024Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Treescited by 30
  21. 2023Large Language Models as Topological Structure Enhancers for Text-Attributed Graphscited by 27
  22. 2024DTGB: A Comprehensive Benchmark for Dynamic Text-Attributed Graphscited by 26
  23. 2024Unified Parameter-Efficient Unlearning for LLMscited by 24
  24. 2024UniMoT: Unified Molecule-Text Language Model with Discrete Token Representationcited by 23
  25. 2025Graphs Meet AI Agents: Taxonomy, Progress, and Future Opportunitiescited by 23
  26. 2024A survey of dynamic graph neural networkscited by 21
  27. 2025When Do LLMs Help With Node Classification? A Comprehensive Analysiscited by 21
  28. 2025LLM as GNN: Graph Vocabulary Learning for Text-Attributed Graph Foundation Modelscited by 21
  29. 2024Graph-oriented Instruction Tuning of Large Language Models for Generic Graph Miningcited by 20
  30. 2024When Graph meets Multimodal: Benchmarking and Meditating on Multimodal Attributed Graphs Learningcited by 20
  31. 2024LLMs and the Future of Chip Design: Unveiling Security Risks and Building Trustcited by 18
  32. 2024ALPINE: Unveiling the Planning Capability of Autoregressive Learning in Language Modelscited by 18
  33. 2025Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Surveycited by 18
  34. 2024A Survey of Large Language Models on Generative Graph Analytics: Query, Learning, and Applicationscited by 17
  35. 2024HIGHT: Hierarchical Graph Tokenization for Molecule-Language Alignmentcited by 17
  36. 2025GRAPHGPT-O: Synergistic Multimodal Comprehension and Generation on Graphscited by 17
  37. 2024Advancing Graph Representation Learning with Large Language Models: A Comprehensive Survey of Techniquescited by 16
  38. 2024Exploring the Potential of Large Language Models in Graph Generationcited by 15

50 most cited of 223 All 223 (JSON)