Graph foundation model

Chuan Shi

doi:10.1007/s11704-024-40046-0

2024-07-05

PRE

DOI：10.1007/s11704-024-40046-0

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摘要

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ConclusionGraph 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.