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Unifying community detection and network embedding in attributed networks

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Abstract

Traditionally, community detection and network embedding are two separate tasks. Network embedding aims to output a vector representation for each node in the network, and community detection aims to find all densely connected groups of nodes and well separate them from others. Most of the existing approaches do community detection and network embedding in a separate manner, and ignore node attributes information, which leads to poor results. In this paper, we propose a novel model that jointly solves the network embedding and community detection problems together. The model can make use of the network local information, the global information and node attributes information collaboratively. We empirically show that by jointly solving these two problems together, the model can greatly improve the ability of community detection, but also learn better network embedding than the advanced baseline methods. We evaluate the proposed model on several datasets, and the experimental results have shown the effectiveness and advancement of our model.

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Notes

  1. https://linqs.soe.ucsc.edu/data.

  2. http://mlg.ucd.ie/networks/.

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Acknowledgements

Our work is supported by the National Key Research Development Program of China (No. 2017YFB0802800). The authors would like to thank the Editor-in-Chief and anonymous reviewers for their insightful and constructive commendations that have led to an improved version of this paper.

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Authors and Affiliations

  1. Institute of Command and Control Engineering, Army Engineering University of PLA, Nanjing, 210007, China

    Yu Ding, Guyu Hu, Zhisong Pan & Shuaihui Wang

  2. National Key Laboratory of Science and Technology on Bind Signal Processing, Chengdu, Sichuan, China

    Hao Wei

  3. Jiangsu Broadcasting Corporation, Nanjing, 210000, China

    Yu Ding

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  1. Yu Ding
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  2. Hao Wei
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  3. Guyu Hu
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  4. Zhisong Pan
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  5. Shuaihui Wang
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Correspondence to Hao Wei.

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Ding, Y., Wei, H., Hu, G. et al. Unifying community detection and network embedding in attributed networks. Knowl Inf Syst 63, 1221–1239 (2021). https://doi.org/10.1007/s10115-021-01557-5

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