  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.
