A collection of AWESOME things about Graph-Related Large Language Models (LLMs).
Large Language Models (LLMs) have shown remarkable progress in natural language processing tasks. However, their integration with graph structures, which are prevalent in real-world applications, remains relatively unexplored. This repository aims to bridge that gap by providing a curated list of research papers that explore the intersection of graph-based techniques with LLMs.
- (NAACL 2021) Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training [paper][code]
- (NeurIPS'23) Can Language Models Solve Graph Problems in Natural Language? [paper][code]
- (arXiv 2023.05) GPT4Graph: Can Large Language Models Understand Graph Structured Data? An Empirical Evaluation and Benchmarking [paper][code]
- (IEEE Intelligent Systems 2023) Integrating Graphs with Large Language Models: Methods and Prospects [paper]
- (arXiv 2023.08) Graph Meets LLMs: Towards Large Graph Models [paper]
- (arXiv 2023.10) Towards Graph Foundation Models: A Survey and Beyond [paper]
- (arXiv 2023.10) LLM4DyG: Can Large Language Models Solve Problems on Dynamic Graphs? [paper]
- (arXiv 2023.11) Can Knowledge Graphs Reduce Hallucinations in LLMs? : A Survey [paper]
- (arXiv 2023.11) A Survey of Graph Meets Large Language Model: Progress and Future Directions [paper][code]
- (arXiv 2023.12) Large Language Models on Graphs: A Comprehensive Survey [paper]
- (arXiv 2023.05) PiVe: Prompting with Iterative Verification Improving Graph-based Generative Capability of LLMs [paper][code]
- (arXiv 2023.05) StructGPT: A General Framework for Large Language Model to Reason over Structured Data [paper][code]
- (arXiv 2023.08) Graph of Thoughts: Solving Elaborate Problems with Large Language Models [paper][code]
- (arXiv 2023.08) Boosting Logical Reasoning in Large Language Models through a New Framework: The Graph of Thought [paper]
- (arxiv 2023.10) Thought Propagation: An Analogical Approach to Complex Reasoning with Large Language Models [paper]
- (arXiv 2023.08) Natural Language is All a Graph Needs [paper][code]
- (arXiv 2023.10) One for All: Towards Training One Graph Model for All Classification Tasks [paper][code]
- (arXiv 2023.10) GraphGPT: Graph Instruction Tuning for Large Language Models [paper][code][blog in Chinese]
- (arXiv 2023.10) Graph Agent: Explicit Reasoning Agent for Graphs [paper]
- (NeurIPS'23) GraphAdapter: Tuning Vision-Language Models With Dual Knowledge Graph [paper][code]
- (arXiv 2023.10) Multimodal Graph Learning for Generative Tasks [paper][code]
- (arXiv 2023.04) Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Augmented by ChatGPT [paper][code]
- (arXiv 2023.10) GraphText: Graph Reasoning in Text Space [paper]
- (arXiv 2023.10) GraphLLM: Boosting Graph Reasoning Ability of Large Language Model [paper][code]
- (arXiv 2023.05) Explanations as Features: LLM-Based Features for Text-Attributed Graphs [paper][code]
- (arXiv 2023.07) Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs [paper] [code]
- (arXiv 2023.09) Can LLMs Effectively Leverage Structural Information for Graph Learning: When and Why [paper][code]
- (arXiv 2023.10) Label-free Node Classification on Graphs with Large Language Models (LLMS) [paper]
- (arXiv 2023.10) Empower Text-Attributed Graphs Learning with Large Language Models (LLMs) [paper]
- (arXiv 2023.10) Disentangled Representation Learning with Large Language Models for Text-Attributed Graphs [paper]
- (arXiv 2023.11) Large Language Models as Topological Structure Enhancers for Text-Attributed Graphs [paper]
- (arXiv 2023.06) GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learning [paper] [code]
- (arXiv 2023.07) Can Large Language Models Empower Molecular Property Prediction? [paper] [code]
- (AAAI 2022) Enhanced Story Comprehension for Large Language Models through Dynamic Document-Based Knowledge Graphs [paper]
- (EMNLP 2022) Language Models of Code are Few-Shot Commonsense Learners [paper][code]
- (SIGIR 2023) Schema-aware Reference as Prompt Improves Data-Efficient Knowledge Graph Construction [paper][code]
- (arXiv 2023.04) CodeKGC: Code Language Model for Generative Knowledge Graph Construction [paper][code]
- (arXiv 2023.05) Knowledge Graph Completion Models are Few-shot Learners: An Empirical Study of Relation Labeling in E-commerce with LLMs [paper]
- (arXiv 2023.07) AutoAlign: Fully Automatic and Effective Knowledge Graph Alignment enabled by Large Language Models [paper][code]
- (arXiv 2023.09) Graph Neural Prompting with Large Language Models [paper]
- (arXiv 2023.10) Faithful Path Language Modelling for Explainable Recommendation over Knowledge Graph [paper]
- (arXiv 2023.11) Zero-Shot Relational Learning on Temporal Knowledge Graphs with Large Language Models [paper]
- (arXiv 2023.03) Ask and You Shall Receive (a Graph Drawing): Testing ChatGPT’s Potential to Apply Graph Layout Algorithms [paper]
- (arXiv 2023.05) Graph Meets LLM: A Novel Approach to Collaborative Filtering for Robust Conversational Understanding [paper]
- (arXiv 2023.05) ChatGPT Informed Graph Neural Network for Stock Movement Prediction [paper][code]
- (arXiv 2023.10) Graph Neural Architecture Search with GPT-4 [paper]
- (WSDM 2024) LLMRec: Large Language Models with Graph Augmentation for Recommendation [paper] [code] [blog in Chinese].
- (arXiv 2023.11) Graph-Guided Reasoning for Multi-Hop Question Answering in Large Language Models [paper]
- GraphGPT: Extrapolating knowledge graphs from unstructured text using GPT-3
- GraphML: Graph markup language. An XML-based file format for graphs.
- GML: Graph modelling language. Read graphs in GML format.
👍 Contributions to this repository are welcome!
If you have come across relevant resources, feel free to open an issue or submit a pull request.
- (*journal*) paper_name [[pdf]](link)[[code]](link)