Graph self-supervised learning is now a go-to method for pre-training graph
foundation models, including graph neural networks, graph transformers, and
more recent large language model (LLM)-based graph models. There is a wide
variety of knowledge patterns embedded in the structure and properties of
graphs which may be used for pre-training, but we lack a systematic overview of
self-supervised pre-training tasks from the perspective of graph knowledge. In
this paper, we comprehensively survey and analyze the pre-training tasks of
graph foundation models from a knowledge-based perspective, consisting of
microscopic (nodes, links, etc) and macroscopic knowledge (clusters, global
structure, etc). It covers a total of 9 knowledge categories and 25
pre-training tasks, as well as various downstream task adaptation strategies.
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这篇论文综合了基于图知识的微观和宏观视角，对图基础模型的自我监督预训练任务进行了综述和分析，并包括了 9 个知识类别和 25 个预训练任务，以及各种下游任务适应策略。