A Survey of Large Language Models for Graphs

• URL: http://arxiv.org/abs/2405.08011v3
• Date: Wed, 11 Sep 2024 07:31:29 GMT
• Title: A Survey of Large Language Models for Graphs
• Authors: Xubin Ren, Jiabin Tang, Dawei Yin, Nitesh Chawla, Chao Huang,
• Abstract summary: We conduct an in-depth review of the latest state-of-the-art Large Language Models applied in graph learning. We introduce a novel taxonomy to categorize existing methods based on their framework design. We explore the strengths and limitations of each framework, and emphasize potential avenues for future research.
• Score: 21.54279919476072
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graphs are an essential data structure utilized to represent relationships in real-world scenarios. Prior research has established that Graph Neural Networks (GNNs) deliver impressive outcomes in graph-centric tasks, such as link prediction and node classification. Despite these advancements, challenges like data sparsity and limited generalization capabilities continue to persist. Recently, Large Language Models (LLMs) have gained attention in natural language processing. They excel in language comprehension and summarization. Integrating LLMs with graph learning techniques has attracted interest as a way to enhance performance in graph learning tasks. In this survey, we conduct an in-depth review of the latest state-of-the-art LLMs applied in graph learning and introduce a novel taxonomy to categorize existing methods based on their framework design. We detail four unique designs: i) GNNs as Prefix, ii) LLMs as Prefix, iii) LLMs-Graphs Integration, and iv) LLMs-Only, highlighting key methodologies within each category. We explore the strengths and limitations of each framework, and emphasize potential avenues for future research, including overcoming current integration challenges between LLMs and graph learning techniques, and venturing into new application areas. This survey aims to serve as a valuable resource for researchers and practitioners eager to leverage large language models in graph learning, and to inspire continued progress in this dynamic field. We consistently maintain the related open-source materials at \url{https://github.com/HKUDS/Awesome-LLM4Graph-Papers}.

Related papers

• NT-LLM: A Novel Node Tokenizer for Integrating Graph Structure into Large Language Models [26.739650151993928]
  Graphs are a fundamental data structure for representing relationships in real-world scenarios. Applying Large Language Models (LLMs) to graph-related tasks poses significant challenges. We introduce Node Tokenizer for Large Language Models (NT-LLM), a novel framework that efficiently encodes graph structures.
  arXiv  Detail & Related papers  (2024-10-14T17:21:57Z)
• 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)
• Graph Machine Learning in the Era of Large Language Models (LLMs) [44.25731266093967]
  Graphs play an important role in representing complex relationships in various domains like social networks, knowledge graphs, and molecular discovery. With the advent of deep learning, Graph Neural Networks (GNNs) have emerged as a cornerstone in Graph Machine Learning (Graph ML) Recently, LLMs have demonstrated unprecedented capabilities in language tasks and are widely adopted in a variety of applications such as computer vision and recommender systems.
  arXiv  Detail & Related papers  (2024-04-23T11:13:39Z)
• A Survey of Large Language Models on Generative Graph Analytics: Query, Learning, and Applications [4.777453721753589]
  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.
  arXiv  Detail & Related papers  (2024-04-23T07:39:24Z)
• 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)
• Large Language Models on Graphs: A Comprehensive Survey [77.16803297418201]
  We provide a systematic review of scenarios and techniques related to large language models on graphs. We first summarize potential scenarios of adopting LLMs on graphs into three categories, namely pure graphs, text-attributed graphs, and text-paired graphs. We discuss the real-world applications of such methods and summarize open-source codes and benchmark datasets.
  arXiv  Detail & Related papers  (2023-12-05T14:14:27Z)
• A Survey of Graph Meets Large Language Model: Progress and Future Directions [38.63080573825683]
  Large Language Models (LLMs) have achieved tremendous success in various domains. LLMs have been leveraged in graph-related tasks to surpass traditional Graph Neural Networks (GNNs) based methods.
  arXiv  Detail & Related papers  (2023-11-21T07:22:48Z)
• 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)
• Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs [59.74814230246034]
  Large Language Models (LLMs) have been proven to possess extensive common knowledge and powerful semantic comprehension abilities. We investigate two possible pipelines: LLMs-as-Enhancers and LLMs-as-Predictors.
  arXiv  Detail & Related papers  (2023-07-07T05:31:31Z)
• Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning [51.90524745663737]
  A key innovation is our use of explanations as features, which can be used to boost GNN performance on downstream tasks. Our method achieves state-of-the-art results on well-established TAG datasets. Our method significantly speeds up training, achieving a 2.88 times improvement over the closest baseline on ogbn-arxiv.
  arXiv  Detail & Related papers  (2023-05-31T03:18:03Z)

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.