A Survey of Large Language Models on Generative Graph Analytics: Query, Learning, and Applications

• URL: http://arxiv.org/abs/2404.14809v1
• Date: Tue, 23 Apr 2024 07:39:24 GMT
• Title: A Survey of Large Language Models on Generative Graph Analytics: Query, Learning, and Applications
• Authors: Wenbo Shang, Xin Huang,
• Abstract summary: Large language models (LLMs) have showcased a strong generalization ability to handle various NLP and multi-mode tasks. Compared with graph learning models, LLMs enjoy superior advantages in addressing the challenges of generalizing graph tasks. We study the key problems of LLM-based generative graph analytics (LLM-GGA) with three categories.
• Score: 4.777453721753589
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 survey, we conduct a comprehensive investigation of existing LLM studies on graph data, which summarizes the relevant graph analytics tasks solved by advanced LLM models and points out the existing remaining challenges and future directions. Specifically, we study the key problems of LLM-based generative graph analytics (LLM-GGA) with three categories: LLM-based graph query processing (LLM-GQP), LLM-based graph inference and learning (LLM-GIL), and graph-LLM-based applications. LLM-GQP focuses on an integration of graph analytics techniques and LLM prompts, including graph understanding and knowledge graph (KG) based augmented retrieval, while LLM-GIL focuses on learning and reasoning over graphs, including graph learning, graph-formed reasoning and graph representation. We summarize the useful prompts incorporated into LLM to handle different graph downstream tasks. Moreover, we give a summary of LLM model evaluation, benchmark datasets/tasks, and a deep pro and cons analysis of LLM models. We also explore open problems and future directions in this exciting interdisciplinary research area of LLMs and graph analytics.

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