{"title":"Graph foundation model","authors":"Chuan Shi, Junze Chen, Jiawei Liu, Cheng Yang","doi":"10.1007/s11704-024-40046-0","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":12640,"journal":{"name":"Frontiers of Computer Science","volume":"16 1","pages":""},"PeriodicalIF":3.4000,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers of Computer Science","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s11704-024-40046-0","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
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.
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.
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