  The last years have witnessed the emergence of a promising self-supervised
learning strategy, referred to as masked autoencoding. However, there is a lack
of theoretical understanding of how masking matters on graph autoencoders
(GAEs). In this work, we present masked graph autoencoder (MaskGAE), a
self-supervised learning framework for graph-structured data. Different from
standard GAEs, MaskGAE adopts masked graph modeling (MGM) as a principled
pretext task - masking a portion of edges and attempting to reconstruct the
missing part with partially visible, unmasked graph structure. To understand
whether MGM can help GAEs learn better representations, we provide both
theoretical and empirical evidence to comprehensively justify the benefits of
this pretext task. Theoretically, we establish close connections between GAEs
and contrastive learning, showing that MGM significantly improves the
self-supervised learning scheme of GAEs. Empirically, we conduct extensive
experiments on a variety of graph benchmarks, demonstrating the superiority of
MaskGAE over several state-of-the-arts on both link prediction and node
classification tasks.
