Awesome-Graph-LLM

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.

Table of Contents

• Awesome-Graph-LLM
  • Table of Contents
  • Datasets, Benchmarks & Surveys
  • Prompting
  • General Graph Model
  • Large Multimodal Models (LMMs)
  • Applications
    • Basic Graph Reasoning
    • Node Classification
    • Graph Classification/Regression
    • Knowledge Graph
    • Others
  • Resources & Tools
  • Contributing

Datasets, Benchmarks & Surveys

• (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 2023.10) Towards Graph Foundation Models: A Survey and Beyond [paper]
• (arXiv 2023.10) LLM4DyG: Can Large Language Models Solve Problems on Dynamic Graphs? [paper]

Prompting

• (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]

General Graph Model

• (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]

Large Multimodal Models (LMMs)

• (NeurIPS'23) GraphAdapter: Tuning Vision-Language Models With Dual Knowledge Graph [paper][code]
• (arXiv 2023.10) Multimodal Graph Learning for Generative Tasks [paper][code]

Applications

Basic Graph Reasoning

• (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]

Node Classification

• (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]

Graph Classification/Regression

• (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]

Knowledge Graph

• (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 2023.09) Graph Neural Prompting with Large Language Models [paper]
• (arXiv 2023.10) Faithful Path Language Modelling for Explainable Recommendation over Knowledge Graph [paper]

Others

• (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].

Resources & Tools

• 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.

Contributing

👍 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)