Graph foundation model

IF 3.4 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Frontiers of Computer Science Pub Date : 2024-07-05 DOI:10.1007/s11704-024-40046-0

Chuan Shi, Junze Chen, Jiawei Liu, Cheng Yang

引用次数: 0

Abstract

Graph Foundation Models represent an evolving direction in graph machine learning. Drawing inspiration from the success of Large Language Models in NLP, GFMs are designed to be trained on extensive graph data and adapted for a diverse array of downstream tasks. In this article, we have explained and introduced the concept of GFMs, comparing them with Language Foundation Models to highlight their similarities and differences. We identified the key technologies in building GFMs as the pre-train and adaptation techniques from the fields of GNNs and LLMs. Additionally, we discussed the potential for GFMs to have significant applications in various domains, ranging from social network analysis to bioinformatics and beyond.

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图形基础模型

图形基础模型代表了图形机器学习的发展方向。图基础模型从大型语言模型在 NLP 领域的成功中汲取灵感，旨在对大量图数据进行训练，并适用于各种下游任务。在本文中，我们解释并介绍了 GFMs 的概念，并将其与语言基础模型进行了比较，以突出它们之间的异同。我们确定了构建 GFMs 的关键技术，即来自 GNN 和 LLMs 领域的预训练和适应技术。此外，我们还讨论了 GFMs 在从社交网络分析到生物信息学等各个领域的重要应用潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Frontiers of Computer Science COMPUTER SCIENCE, INFORMATION SYSTEMS-COMPUTER SCIENCE, SOFTWARE ENGINEERING

CiteScore

8.60

自引率

2.40%

发文量

799

审稿时长

6-12 weeks

期刊介绍： Frontiers of Computer Science aims to provide a forum for the publication of peer-reviewed papers to promote rapid communication and exchange between computer scientists. The journal publishes research papers and review articles in a wide range of topics, including: architecture, software, artificial intelligence, theoretical computer science, networks and communication, information systems, multimedia and graphics, information security, interdisciplinary, etc. The journal especially encourages papers from new emerging and multidisciplinary areas, as well as papers reflecting the international trends of research and development and on special topics reporting progress made by Chinese computer scientists.