Self-supervised Consensus Representation Learning for Attributed Graph. (arXiv:2108.04822v1 [cs.SI])

Attempting to fully exploit the rich information of topological structure and
node features for attributed graph, we introduce self-supervised learning
mechanism to graph representation learning and propose a novel Self-supervised
Consensus Representation Learning (SCRL) framework. In contrast to most
existing works that only explore one graph, our proposed SCRL method treats
graph from two perspectives: topology graph and feature graph. We argue that
their embeddings should share some common information, which could serve as a
supervisory signal. Specifically, we construct the feature graph of node
features via k-nearest neighbor algorithm. Then graph convolutional network
(GCN) encoders extract features from two graphs respectively. Self-supervised
loss is designed to maximize the agreement of the embeddings of the same node
in the topology graph and the feature graph. Extensive experiments on real
citation networks and social networks demonstrate the superiority of our
proposed SCRL over the state-of-the-art methods on semi-supervised node
classification task. Meanwhile, compared with its main competitors, SCRL is
rather efficient.



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