MGAE: Masked Autoencoders for Self-Supervised Learning on Graphs
It addresses graph representation learning for tasks like link prediction and node classification, offering an incremental improvement over existing methods.
The paper tackles self-supervised learning on graphs by introducing a masked graph autoencoder (MGAE) that reconstructs randomly masked edges, achieving better performance than state-of-the-art unsupervised methods on link prediction and node classification across multiple benchmarks.
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 encoder to perform message propagation on the partially-masked graph. To reconstruct the large number of masked edges, a tailored cross-correlation decoder is proposed. It could capture the cross-correlation between the head and tail nodes of anchor edge in multi-granularity. Coupling these two designs enables MGAE to be trained efficiently and effectively. Extensive experiments on multiple open datasets (Planetoid and OGB benchmarks) demonstrate that MGAE generally performs better than state-of-the-art unsupervised learning competitors on link prediction and node classification.