Semi-supervised overlapping community detection in attributed graph with graph convolutional autoencoder

[Display omitted] The architecture of our proposed method SSGCAE for semi-supervised overlapping community detection in attributed graph. •An end-to-end method SSGCAE for overlapping community detection is proposed.•SSGCAE is based on graph convolutional autoencoder driven by community detection.•SS...

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Veröffentlicht in:Information sciences Jg. 608; S. 1464 - 1479
Hauptverfasser: He, Chaobo, Zheng, Yulong, Cheng, Junwei, Tang, Yong, Chen, Guohua, Liu, Hai
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Inc 01.08.2022
Schlagworte:
Attributed graph
Graph convolutional autoencoder
Graph neural networks
Overlapping community detection
Semi-supervised learning
Graph neural networks
Graph convolutional autoencoder
Semi-supervised learning
Attributed graph
Overlapping community detection
ISSN:0020-0255, 1872-6291
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Zusammenfassung:[Display omitted] The architecture of our proposed method SSGCAE for semi-supervised overlapping community detection in attributed graph. •An end-to-end method SSGCAE for overlapping community detection is proposed.•SSGCAE is based on graph convolutional autoencoder driven by community detection.•SSGCAE is comprehensively evaluated on synthetic and real attributed graphs.•SSGCAE outperforms state-of-the-art baselines. Community detection in attributed graph is of great application value and many related methods have been continually presented. However, existing methods for community detection in attributed graph still cannot well solve three key problems simultaneously: link information and attribute information fusion, prior information integration and overlapping community detection. Aiming at these problems, in this paper we devise a semi-supervised overlapping community detection method named SSGCAE which is based on graph neural networks. This method is composed of three modules: graph convolutional autoencoder (GCAE), semi-supervision and modularity maximization, which are respectively utilized to fuse link information and attribute information, integrate prior information and detect overlapping communities. We treat GCAE as the backbone framework and train it by using the unified loss from these three modules. Through this way, these three modules are jointly correlated via the community membership representation, which is very beneficial to improve the overall performance. SSGCAE is comprehensively evaluated on synthetic and real attributed graphs, and experiment results show that it is very effective and outperforms state-of-the-art baseline approaches.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2022.07.036