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Exploring the Potential of Large Language Models (LLMs)in Learning on Graphs

Authors:

Shuaiqiang Wang,

Jiliang TangAuthors Info & Claims

ACM SIGKDD Explorations Newsletter, Volume 25, Issue 2

Pages 42 - 61

https://doi.org/10.1145/3655103.3655110

Published: 28 March 2024 Publication History

Abstract

Learning on Graphs has attracted immense attention due to its wide real-world applications. The most popular pipeline for learning on graphs with textual node attributes primarily relies on Graph Neural Networks (GNNs), and utilizes shallow text embedding as initial node representations, which has limitations in general knowledge and profound semantic understanding. In recent years, Large Language Models (LLMs) have been proven to possess extensive common knowledge and powerful semantic comprehension abilities that have revolutionized existing workflows to handle text data. In this paper, we aim to explore the potential of LLMs in graph machine learning, especially the node classification task, and investigate two possible pipelines: LLMs-as-Enhancers and LLMs-as-Predictors. The former leverages LLMs to enhance nodes' text attributes with their massive knowledge and then generate predictions through GNNs. The latter attempts to directly employ LLMs as standalone predictors. We conduct comprehensive and systematical studies on these two pipelines under various settings. From comprehensive empirical results, we make original observations and find new insights that open new possibilities and suggest promising directions to leverage LLMs for learning on graphs. Our codes and datasets are available at: https://github.com/CurryTang/Graph-LLM .

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Zhang ZWang XQin YChen HZhang ZChu XZhu WSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Disentangled continual graph neural architecture search with invariant modular supernetProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3694549(59975-59991)Online publication date: 21-Jul-2024
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Morris CFrasca FDym NMaron HCeylan İLevie RLim DBronstein MGrohe MJegelka SSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)PositionProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693547(36294-36307)Online publication date: 21-Jul-2024
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Index Terms

Exploring the Potential of Large Language Models (LLMs)in Learning on Graphs
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information systems applications

Index terms have been assigned to the content through auto-classification.

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Published In

cover image ACM SIGKDD Explorations Newsletter

ACM SIGKDD Explorations Newsletter Volume 25, Issue 2

December 2023

58 pages

ISSN:1931-0145

EISSN:1931-0153

DOI:10.1145/3655103

Issue’s Table of Contents

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