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- W4200317246 abstract "It is challenging to jointly model dual heterogenous spaces including network topologies and node attributes, especially in the lack of label guidance. Our experimental investigation clearly shows the similarity matrix derived from either the topological or attributed space is distant from the optimal block-diagonal structure, thus it does not reveal salient intra-community compactness. This weakness may severely hinder the capability of recent emerging multi-channel architectures, since some of those channels depending on solo information are in-sufficient. In this paper, we propose a neat dual-channel network embedding architecture. In each channel, attribute information propagates over network topology to node embedding via two independent message-passing manners. To this end, we introduce a graph convolutional filter based simplified symmetric graph autoencoder module. Moreover, a dual self-supervised mechanism acts as consistency regularization by leveraging the soft clustering assignment distribution consistency of unlabeled nodes in different channels, guiding the update of the whole model. Our extensive experiments demonstrate the superior performance of this new method over state-of-the-art methods." @default.
- W4200317246 created "2021-12-31" @default.
- W4200317246 creator A5015469468 @default.
- W4200317246 creator A5083403017 @default.
- W4200317246 date "2021-09-17" @default.
- W4200317246 modified "2023-10-17" @default.
- W4200317246 title "Dual Graph Convolution for Attributed Graph Clustering" @default.
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- W4200317246 doi "https://doi.org/10.1145/3490725.3490744" @default.
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