mlsanityData to 7 Oct 2026

GraphEdit: Large Language Models for Graph Structure Learning

arXiv 2402.15183 ↗First posted 23 Feb 2024cs.LG cs.AIcited by 33

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Abstract (arXiv)

Graph Structure Learning (GSL) focuses on capturing intrinsic dependencies and interactions among nodes in graph-structured data by generating novel graph structures. Graph Neural Networks (GNNs) have emerged as promising GSL solutions, utilizing recursive message passing to encode node-wise inter-dependencies. However, many existing GSL methods heavily depend on explicit graph structural information as supervision signals, leaving them susceptible to challenges such as data noise and sparsity. In this work, we propose GraphEdit, an approach that leverages large language models (LLMs) to learn complex node relationships in graph-structured data. By enhancing the reasoning capabilities of LLMs through instruction-tuning over graph structures, we aim to overcome the limitations associated with explicit graph structural information and enhance the reliability of graph structure learning. Our approach not only effectively denoises noisy connections but also identifies node-wise dependencies from a global perspective, providing a comprehensive understanding of the graph structure. We conduct extensive experiments on multiple benchmark datasets to demonstrate the effectiveness and robustness of GraphEdit across various settings. We have made our model implementation available at: https://github.com/HKUDS/GraphEdit.

Key facts. Cited by 33 papers in the map (data through 7 Oct 2026). Builds on 30 of its 39 references that are in the map. First posted on arXiv 23 Feb 2024.

Builds on

30 of 39 references.

References · 30

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  3. 2021GLM: General Language Model Pretraining with Autoregressive Blank Infillingcited by 1,026
  4. 2019DropEdge: Towards Deep Graph Convolutional Networks on Node Classificationcited by 679
  5. 2020Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classificationcited by 500
  6. 2020Graph Structure Learning for Robust Graph Neural Networkscited by 368
  7. 2023Exploring the Potential of Large Language Models (LLMs) in Learning on Graphscited by 271
  8. 2019ERNIE 2.0: A Continual Pre-training Framework for Language Understandingcited by 230
  9. 2019Learning Discrete Structures for Graph Neural Networkscited by 228
  10. 2020Generalization and Representational Limits of Graph Neural Networkscited by 217
  11. 2020Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddingscited by 212
  12. 2023NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classificationcited by 207
18 more
  1. 2020Data Augmentation for Graph Neural Networkscited by 200
  2. 2021Is Homophily a Necessity for Graph Neural Networks?cited by 182
  3. 2023LLMRec: Large Language Models with Graph Augmentation for Recommendationcited by 171
  4. 2023Representation Learning with Large Language Models for Recommendationcited by 155
  5. 2022Towards Unsupervised Deep Graph Structure Learningcited by 112
  6. 2021SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networkscited by 108
  7. 2022BLOOM+1: Adding Language Support to BLOOM for Zero-Shot Promptingcited by 90
  8. 2022Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labelscited by 63
  9. 2023Augmenting Low-Resource Text Classification with Graph-Grounded Pre-training and Promptingcited by 62
  10. 2019Graph-Revised Convolutional Networkcited by 58
  11. 2021Deconfounded Recommendation for Alleviating Bias Amplificationcited by 51
  12. 2023SE-GSL: A General and Effective Graph Structure Learning Framework through Structural Entropy Optimizationcited by 35
  13. 2022Reliable Representations Make A Stronger Defender: Unsupervised Structure Refinement for Robust GNNcited by 34
  14. 2021Task-adaptive Neural Process for User Cold-Start Recommendationcited by 32
  15. 2023OpenGSL: A Comprehensive Benchmark for Graph Structure Learningcited by 30
  16. 2022Graph Neural Networks with Adaptive Readoutscited by 29
  17. 2022Compact Graph Structure Learning via Mutual Information Compressioncited by 20
  18. 2022Restructuring Graph for Higher Homophily via Adaptive Spectral Clusteringcited by 9

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 13; 2025: 16; 2026: 4.

33 papers.

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  1. 2024OpenGraph: Towards Open Graph Foundation Modelscited by 75
  2. 2024A Survey of Large Language Models for Graphscited by 71
  3. 2024Knowledge Graph Large Language Model (KG-LLM) for Link Predictioncited by 36
  4. 2024Graph Machine Learning in the Era of Large Language Models (LLMs)cited by 33
  5. 2024Can Large Language Models Improve the Adversarial Robustness of Graph Neural Networks?cited by 22
  6. 2025LLM as GNN: Graph Vocabulary Learning for Text-Attributed Graph Foundation Modelscited by 21
  7. 2025GraphMaster: Automated Graph Synthesis via LLM Agents in Data-Limited Environmentscited by 18
  8. 2025Graph2text or Graph2token: A Perspective of Large Language Models for Graph Learningcited by 12
  9. 2024An Automatic Graph Construction Framework based on Large Language Models for Recommendationcited by 8
  10. 2024LangTopo: Aligning Language Descriptions of Graphs with Tokenized Topological Modelingcited by 7
  11. 2024Multi-View Empowered Structural Graph Wordification for Language Modelscited by 7
  12. 2025Few-Shot Graph Out-of-Distribution Detection with LLMscited by 6
21 more
  1. 2024Graph Reasoning with Large Language Models via Pseudo-code Promptingcited by 5
  2. 2025Large Language Models Meet Text-Attributed Graphs: A Survey of Integration Frameworks and Applicationscited by 5
  3. 2025Structural Priors and Modular Adapters in the Composable Fine-Tuning Algorithm of Large-Scale Modelscited by 4
  4. 2024Large Language Models for Link Stealing Attacks Against Graph Neural Networkscited by 3
  5. 2024Enhancing Graph Neural Networks with Limited Labeled Data by Actively Distilling Knowledge from Large Language Modelscited by 3
  6. 2024Bridging Large Language Models and Graph Structure Learning Models for Robust Representation Learningcited by 3
  7. 2025TinyGraphEstimator: Adapting Lightweight Language Models for Graph Structure Inferencecited by 3
  8. 2025Refining Interactions: Enhancing Anisotropy in Graph Neural Networks with Language Semanticscited by 2
  9. 2025Global-Recent Semantic Reasoning on Dynamic Text-Attributed Graphs with Large Language Modelscited by 2
  10. 2025Towards Efficient LLM-aware Heterogeneous Graph Learningcited by 2
  11. 2025Trustworthy GNNs with LLMs: A Systematic Review and Taxonomycited by 1
  12. 2025Robustness in Text-Attributed Graph Learning: Insights, Trade-offs, and New Defensescited by 1
  13. 2026Integrating Graphs, Large Language Models, and Agents: Reasoning and Retrievalcited by 1
  14. 2024Large Language Models Merging for Enhancing the Link Stealing Attack on Graph Neural Networkscited by 0
  15. 2025Navigating the Black Box: Leveraging LLMs for Effective Text-Level Graph Injection Attackscited by 0
  16. 2025Enhancing Spectral Graph Neural Networks with LLM-Predicted Homophilycited by 0
  17. 2025Bridging the Divide: End-to-End Sequence-Graph Learningcited by 0
  18. 2025T2T-LA: A Topology-to-Topology LLM Agent for Graph Learning with Neither Feature Access nor Task Knowledgecited by 0
  19. 2026Bridging Graph Structure and Knowledge-Guided Editing for Interpretable Temporal Knowledge Graph Reasoningcited by 0
  20. 2026OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradationcited by 0
  21. 2026Compactness and Consistency: A Conjoint Framework for Deep Graph Clusteringcited by 0

Similar, not cited

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  1. 2024GraphInstruct: Empowering Large Language Models with Graph Understanding and Reasoning Capability
  2. 2025Exploring Graph Learning Tasks with Pure LLMs: A Comprehensive Benchmark and Investigation
  3. 2026Teaching LLMs to See Graphs: Unifying Text and Structural Reasoning
  4. 2024GUNDAM: Aligning Large Language Models with Graph Understanding
  5. 2024Can LLM Graph Reasoning Generalize beyond Pattern Memorization?
  6. 2024GraphEval36K: Benchmarking Coding and Reasoning Capabilities of Large Language Models on Graph Datasets
  7. 2024A Hierarchical Language Model For Interpretable Graph Reasoning
  8. 2026LoReC: Rethinking Large Language Models for Graph Data Analysis