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Graph Masked Autoencoders with Transformers
[article]
2022
arXiv
pre-print
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
arXiv:2202.08391v2
fatcat:2lq5ueqeefgofowogb246ehmwi