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.
Related papers
- GraphICL: Unlocking Graph Learning Potential in LLMs through Structured Prompt Design [13.365623514253926]
Graph In-context Learning (GraphICL) Benchmark is a comprehensive benchmark comprising novel prompt templates to capture graph structure and handle limited label knowledge.
Our systematic evaluation shows that general-purpose LLMs equipped with our GraphICL outperform state-of-the-art specialized graph LLMs and graph neural network models.
arXiv Detail & Related papers (2025-01-27T03:50:30Z) - What Do LLMs Need to Understand Graphs: A Survey of Parametric Representation of Graphs [69.48708136448694]
Large language models (LLMs) are reorganizing in the AI community for their expected reasoning and inference abilities.
We believe this kind of parametric representation of graphs, graph laws, can be a solution for making LLMs understand graph data as the input.
arXiv Detail & Related papers (2024-10-16T00:01:31Z) - How Do Large Language Models Understand Graph Patterns? A Benchmark for Graph Pattern Comprehension [53.6373473053431]
This work introduces a benchmark to assess large language models' capabilities in graph pattern tasks.
We have developed a benchmark that evaluates whether LLMs can understand graph patterns based on either terminological or topological descriptions.
Our benchmark encompasses both synthetic and real datasets, and a variety of models, with a total of 11 tasks and 7 models.
arXiv Detail & Related papers (2024-10-04T04:48:33Z) - Can Large Language Models Analyze Graphs like Professionals? A Benchmark, Datasets and Models [90.98855064914379]
We introduce ProGraph, a benchmark for large language models (LLMs) to process graphs.
Our findings reveal that the performance of current LLMs is unsatisfactory, with the best model achieving only 36% accuracy.
We propose LLM4Graph datasets, which include crawled documents and auto-generated codes based on 6 widely used graph libraries.
arXiv Detail & Related papers (2024-09-29T11:38:45Z) - Can LLM Graph Reasoning Generalize beyond Pattern Memorization? [46.93972334344908]
We evaluate whether large language models (LLMs) can go beyond semantic, numeric, structural, reasoning patterns in the synthetic training data and improve utility on real-world graph-based tasks.
We find that while post-training alignment is most promising for real-world tasks, empowering LLM graph reasoning to go beyond pattern remains an open research question.
arXiv Detail & Related papers (2024-06-23T02:59:15Z) - Exploring the Potential of Large Language Models in Graph Generation [51.046188600990014]
Graph generation requires large language models (LLMs) to generate graphs with given properties.
This paper explores the abilities of LLMs for graph generation with systematical task designs and experiments.
Our evaluations demonstrate that LLMs, particularly GPT-4, exhibit preliminary abilities in graph generation tasks.
arXiv Detail & Related papers (2024-03-21T12:37:54Z) - Can we Soft Prompt LLMs for Graph Learning Tasks? [22.286189757942054]
GraphPrompter is a framework designed to align graph information with Large Language Models (LLMs) via soft prompts.
The framework unveils the substantial capabilities of LLMs as predictors in graph-related tasks.
arXiv Detail & Related papers (2024-02-15T23:09:42Z) - Integrating Graphs with Large Language Models: Methods and Prospects [68.37584693537555]
Large language models (LLMs) have emerged as frontrunners, showcasing unparalleled prowess in diverse applications.
Merging the capabilities of LLMs with graph-structured data has been a topic of keen interest.
This paper bifurcates such integrations into two predominant categories.
arXiv Detail & Related papers (2023-10-09T07:59:34Z) - Beyond Text: A Deep Dive into Large Language Models' Ability on
Understanding Graph Data [13.524529952170672]
Large language models (LLMs) have achieved impressive performance on many natural language processing tasks.
We aim to assess whether LLMs can effectively process graph data and leverage topological structures to enhance performance.
By comparing LLMs' performance with specialized graph models, we offer insights into the strengths and limitations of employing LLMs for graph analytics.
arXiv Detail & Related papers (2023-10-07T23:25:22Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.