MGAE: Masked Autoencoders for Self-Supervised Learning on Graphs. (arXiv:2201.02534v1 [cs.LG])

We introduce a novel masked graph autoencoder (MGAE) framework to perform
effective learning on graph structure data. Taking insights from
self-supervised learning, we randomly mask a large proportion of edges and try
to reconstruct these missing edges during training. MGAE has two core designs.
First, we find that masking a high ratio of the input graph structure, e.g.,
$70\%$, yields a nontrivial and meaningful self-supervisory task that benefits
downstream applications. Second, we employ a graph neural network (GNN) as an …

arxiv graphs learning self-supervised learning supervised learning