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When Does Self-Supervision Help Graph Convolutional Networks?
[article]
2020
arXiv
pre-print
Self-supervision as an emerging technique has been employed to train convolutional neural networks (CNNs) for more transferrable, generalizable, and robust representation learning of images. Its introduction to graph convolutional networks (GCNs) operating on graph data is however rarely explored. In this study, we report the first systematic exploration and assessment of incorporating self-supervision into GCNs. We first elaborate three mechanisms to incorporate self-supervision into GCNs,
arXiv:2006.09136v4
fatcat:iefaimgvxjbsxk4juymdhc5erm