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
  • Prompting
  • Applications
    • Node Classification
    • KG Completion
    • Common Sense Reasoning
    • Others
  • Resources & Tools
  • Contributing

Datasets & Benchmarks

• (NAACL 2021) Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training [paper][code]
• (arXiv 2023.05) 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]

Prompting

• (AAAI 2022) Enhanced Story Comprehension for Large Language Models through Dynamic Document-Based Knowledge Graphs [paper]
• (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 2023.04) Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Augmented by ChatGPT [paper][code]
• (arXiv 2023.05) StructGPT: A General Framework for Large Language Model to Reason over Structured Data [paper][code]
• (arXiv 2023.05) PiVe: Prompting with Iterative Verification Improving Graph-based Generative Capability of LLMs [paper][code]

Applications

Node Classification

• (arXiv 2023.05) Explanations as Features: LLM-Based Features for Text-Attributed Graphs [paper][code]

KG Completion

• (arXiv 2023.05) Knowledge Graph Completion Models are Few-shot Learners: An Empirical Study of Relation Labeling in E-commerce with LLMs [paper]

Common Sense Reasoning

• (EMNLP 2022) Language Models of Code are Few-Shot Commonsense Learners [paper][code]

Others

• (arXiv 2023.03) Ask and You Shall Receive (a Graph Drawing): Testing ChatGPT’s Potential to Apply Graph Layout Algorithms [paper]

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)