Papers › Graph Masked Autoencoders with Transformers

Graph Masked Autoencoders with Transformers

17 Feb 2022arXiv:2202.08391archive 2025-07-28

Sixiao Zhang, Hongxu Chen, Haoran Yang, Xiangguo Sun, Philip S. Yu, Guandong Xu

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.

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rinnesz/gmae officialmentioned in papermentioned on GitHubpytorch report

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Decoder Graph Classification Node Classification

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Absolute Position Encodings Adam Attention BPE Dense Connections Dropout Graph Transformer Label Smoothing LapEigen Laplacian PE Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer Residual Connection SVM Softmax Transformer

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