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LLM-based Generative Graph Analytics (Xin Huang et al.)
A graph is a fundamental data model to represent various entities and their complex relationships in society and nature, such as social networks, transportation networks, financial networks, and biomedical systems. Recently, large language models (LLMs) have showcased a strong generalization ability to handle various NLP and multi-mode tasks to answer users’ arbitrary questions and specific-domain content generation. Compared with graph learning models, LLMs enjoy superior advantages in addressing the challenges of generalizing graph tasks by eliminating the need for training graph learning models and reducing the cost of manual annotation. In this project, we intend to study LLM-based graph analytics and learn unified graph representation to incorporate proposed path features for classical graph learning tasks and also NP-hard graph problems.
References:
- W. Shang, X. Huang: A Survey of Large Language Models on Generative Graph Analytics: Query, Learning, and Applications, arXiv preprint arXiv:2404.14809, 2024
For further information on this research topic, please contact Prof. Xin Huang.
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