Where to Mask: Structure-Guided Masking for Graph Masked Autoencoders

arXiv:2404.15806v1 Announce Type: new
Abstract: Graph masked autoencoders (GMAE) have emerged as a significant advancement in self-supervised pre-training for graph-structured data. Previous GMAE models primarily utilize a straightforward random masking strategy for nodes or edges during training. However, this strategy fails to consider the varying significance of different nodes within the graph structure. In this paper, we investigate the potential of leveraging the graph's structural composition as a fundamental and unique prior in the masked pre-training process. To this end, …

arxiv autoencoders cs.lg graph masking type