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Weakly-supervised learning for community detection based on graph convolution in attributed networks

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Abstract

Community detection in complex networks has been revisited with graph deep learning recently and has attracted great attention. It is often challenging to uncover underlying communities on attributed networks because of the complexity and diversity of graph-structured data. A recent prominent graph deep learning model is graph convolutional network (GCN), which effectively integrates network topology and attribute information in graph representation learning. However, most GCN-based community detection methods are semi-supervised and require a considerable amount of labeled data for training. Here, we propose a weakly-supervised learning method based on GCN for community detection in attributed networks. Our new method integrates the techniques of GCN and label propagation and the latter constructs a balanced label set to uncover underlying community structures with topology and attribute information. The experiments on various real-world networks give a comparison view to evaluate the proposed method. The experimental result demonstrates the proposed method performs more efficiently with a comparative performance over current state-of-the-art community detection algorithms.

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Data Availability Statement

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors would like to thank the reviewers for their insightful comments and useful suggestions. This research was supported by Zhejiang Provincial Natural Science Foundation of China (LQ20F020021 and LY19F030012) and NSAF Joint Fund (No.U20B2048).

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Authors and Affiliations

  1. College of Information Engineering, China Jiliang University, Hangzhou, 310018, China

    Xiaofeng Wang, Li Yang & Hongmei Mi

  2. School of Cyber Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Xiaofeng Wang & Jianhua Li

  3. Faculty of Engineering and Computer Science, Concordia University, Montreal, QC, H3G 1M8, Canada

    Jia Yuan Yu

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  1. Xiaofeng Wang
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  2. Jianhua Li
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  3. Li Yang
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Correspondence to Xiaofeng Wang.

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Wang, X., Li, J., Yang, L. et al. Weakly-supervised learning for community detection based on graph convolution in attributed networks. Int. J. Mach. Learn. & Cyber. 12, 3529–3539 (2021). https://doi.org/10.1007/s13042-021-01400-x

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