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Everything you need to know about Graphfm A Comprehensive Benchmark For Graph Foundation Model Ai. Explore our curated collection and insights below.
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 is now a go-to method for pre-training graph foundation models (GFMs). There is a wide variety of knowledge patterns embedded in the graph data%2C such as node properties and clusters%2C which are crucial to learning generalized representations for GFMs. However%2C existing surveys of GFMs have several shortcomings: they lack comprehensiveness regarding the most recent progress%2C have unclear categorization of self-supervised methods%2C and take a limited architecture-based perspective that is restricted to only certain types of graph models. As the ultimate goal of GFMs is to learn generalized graph knowledge%2C we provide a comprehensive survey of self-supervised GFMs from a novel knowledge-based perspective. We propose a knowledge-based taxonomy%2C which categorizes self-supervised graph models by the specific graph knowledge utilized. Our taxonomy consists of microscopic (nodes%2C links%2C etc.)%2C mesoscopic (context%2C clusters%2C etc.)%2C and macroscopic knowledge (global structure%2C manifolds%2C etc.). It covers a total of 9 knowledge categories and more than 25 pretext tasks for pre-training GFMs%2C as well as various downstream task generalization strategies. Such a knowledge-based taxonomy allows us to re-examine graph models based on new architectures more clearly%2C such as graph language models%2C as well as provide more in-depth insights for constructing GFMs.?quality=80&w=800)
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