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An evolutionary autoencoder for dynamic community detection

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Abstract

Dynamic community detection is significant for controlling and capturing the temporal features of networks. The evolutionary clustering framework provides a temporal smoothness constraint for simultaneously maximizing the clustering quality at the current time step and minimizing the clustering deviation between two successive time steps. Based on this framework, some existing methods, such as the evolutionary spectral clustering and evolutionary nonnegative matrix factorization, aim to look for the low-dimensional representation by mapping reconstruction. However, such reconstruction does not address the nonlinear characteristics of networks. In this paper, we propose a semi-supervised algorithm (sE-Autoencoder) to overcome the effects of nonlinear property on the low-dimensional representation. Our proposed method extends the typical nonlinear reconstruction model to the dynamic network by constructing a temporal matrix. More specifically, the potential community characteristics and the previous clustering, as the prior information, are incorporated into the loss function as a regularization term. Experimental results on synthetic and real-world datasets demonstrate that the proposed method is effective and superior to other methods for dynamic community detection.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Nos. U1803263, 6197618, 11931015, 81961138010) and Natural Science Foundation of Chongqing (Grant Nos. cstc2018jcyjAX0274, cstc2019-jcyj-zdxmX0025), Key Area R&D Program of Guangdong Province (Grant No. 2019B010137004), Key Area R&D Program of Shaanxi Province (Grant No. 2019ZDLGY17-07), and the Fundamental Research Funds for the Central Universities (Grant No. 3102019PJ006).

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Authors and Affiliations

  1. School of Mechanical Engineering and The Center for OPTIMAL, Northwestern Polytechnical University, Xi’an, 710072, China

    Zhen Wang

  2. College of Computer and Information Science, Southwest University, Chongqing, 400715, China

    Chunyu Wang, Chao Gao & Xianghua Li

  3. School of Computer Science and The Center for OPTIMAL, Northwestern Polytechnical University, Xi’an, 710072, China

    Chao Gao, Xuelong Li & Xianghua Li

Authors
  1. Zhen Wang
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  2. Chunyu Wang
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  3. Chao Gao
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  4. Xuelong Li
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  5. Xianghua Li
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Corresponding authors

Correspondence to Xuelong Li or Xianghua Li.

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Wang, Z., Wang, C., Gao, C. et al. An evolutionary autoencoder for dynamic community detection. Sci. China Inf. Sci. 63, 212205 (2020). https://doi.org/10.1007/s11432-020-2827-9

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  • DOI: https://doi.org/10.1007/s11432-020-2827-9

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