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A Survey on Self-Supervised Pre-Training of Graph Foundation Models: A Knowledge-Based Perspective
March 26, 2024, 4:42 a.m. | Ziwen Zhao, Yuhua Li, Yixiong Zou, Ruixuan Li, Rui Zhang
cs.LG updates on arXiv.org arxiv.org
Abstract: 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 …
arxiv cs.lg cs.si foundation graph knowledge perspective pre-training survey training type
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