  Recently, transformers have shown promising performance in learning graph
representations. However, there are still some challenges when applying
transformers to real-world scenarios due to the fact that deep transformers are
hard to train from scratch and the quadratic memory consumption w.r.t. the
number of nodes. In this paper, we propose Graph Masked Autoencoders (GMAEs), a
self-supervised transformer-based model for learning graph representations. To
address the above two challenges, we adopt the masking mechanism and the
asymmetric encoder-decoder design. Specifically, GMAE takes partially masked
graphs as input, and reconstructs the features of the masked nodes. The encoder
and decoder are asymmetric, where the encoder is a deep transformer and the
decoder is a shallow transformer. The masking mechanism and the asymmetric
design make GMAE a memory-efficient model compared with conventional
transformers. We show that, when serving as a conventional self-supervised
graph representation model, GMAE achieves state-of-the-art performance on both
the graph classification task and the node classification task under common
downstream evaluation protocols. We also show that, compared with training in
an end-to-end manner from scratch, we can achieve comparable performance after
pre-training and fine-tuning using GMAE while simplifying the training process.
