Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

5-2024

Abstract

Emerging as fundamental building blocks for diverse artificial intelligence applications, foundation models have achieved notable success across natural language processing and many other domains. Concurrently, graph machine learning has gradually evolved from shallow methods to deep models to leverage the abundant graph-structured data that constitute an important pillar in the data ecosystem for artificial intelligence. Naturally, the emergence and homogenization capabilities of foundation models have piqued the interest of graph machine learning researchers. This has sparked discussions about developing a next-generation graph learning paradigm, one that is pre-trained on broad graph data and can be adapted to a wide range of downstream graph-based tasks. However, there is currently no clear definition or systematic analysis for this type of work.In this tutorial, we will introduce the concept of graph foundation models (GFMs), and provide a comprehensive exposition on their key characteristics and underpinning technologies. Subsequently, we will thoroughly review existing works that lay the groundwork towards GFMs, which are summarized into three primary categories based on their roots in graph neural networks, large language models, or a hybrid of both. Beyond providing a comprehensive overview and in-depth analysis of the current landscape and progress towards graph foundation models, this tutorial will also explore potential avenues for future research in this important and dynamic field. Finally, to help the audience gain a systematic understanding of the topics covered in this tutorial, we present further details in our recent preprint paper, "Towards Graph Foundation Models: A Survey and Beyond"[4], available at https://arxiv.org/pdf/2310.11829.pdf.

Keywords

Graph Foundation Models, Large Language Models, Graph Neural Networks, Deep Learning

Discipline

Graphics and Human Computer Interfaces

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

WWW '24: Companion Proceedings of the ACM Web Conference 2024, Singapore, May 13-17

First Page

1264

Last Page

1267

Identifier

10.1145/3589335.3641246

Publisher

ACM

City or Country

New York

Additional URL

https://doi.org/10.1145/3589335.3641246

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