  Graph self-supervised learning (SSL) 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, such as node properties and clusters, which are
crucial to learning generalized representations for GFMs. However, existing
surveys of GFMs have several shortcomings: they lack comprehensiveness
regarding the most recent progress, have unclear categorization of
self-supervised methods, 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, we provide a comprehensive survey
of self-supervised GFMs from a novel knowledge-based perspective. We propose a
knowledge-based taxonomy, which categorizes self-supervised graph models by the
specific graph knowledge utilized. Our taxonomy consists of microscopic (nodes,
links, etc.), mesoscopic (context, clusters, etc.), and macroscopic knowledge
(global structure, manifolds, etc.). It covers a total of 9 knowledge
categories and more than 25 pretext tasks for pre-training GFMs, 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, such as graph language models, as well as provide more in-depth
insights for constructing GFMs.
