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Community Detection in Dynamic Networks: A Novel Deep Learning Method

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Knowledge Science, Engineering and Management (KSEM 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12815))

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Abstract

Dynamic community detection has become a hot spot of researches, which helps detect the revolving relationships of complex systems. In view of the great value of dynamic community detection, various kinds of dynamic algorithms come into being. Deep learning-based algorithms, as one of the most popular methods, transfer the core ideas of feature representation to dynamic community detection in order to improve the accuracy of dynamic community detection. However, when committing feature aggregation strategies, most of methods focus on the attribute features but omit the structural information of networks, which lowers the accuracy of dynamic community detection. Also, the differences of learned features between adjacent time steps may be large, which does not correspond with the real world. In this paper, we utilize the node relevancy to measure the varying importance of different nodes, which reflects the structural information of networks. Having acquired the node representations at each time step, the cross entropy is used to smoothen adjacent time steps so that the differences between adjacent time steps can be small. Some extensive experiments on both the real-world datasets and synthetic datasets show that our algorithm is more superior than other algorithms.

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Notes

  1. 1.

    https://snap.stanford.edu/data/CollegeMsg.html.

  2. 2.

    http://networkrepository.com/aves-wildbird-network.php.

  3. 3.

    http://snap.stanford.edu/data/soc-sign-bitcoin-otc.html.

  4. 4.

    http://networkrepository.com/dynamic.php.

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Acknowledgment

Prof. Chao Gao and Dr. Li Tao are corresponding authors of this paper. This work was supported by the National Key R&D Program of China (No. 2019YFB2102300), National Natural Science Foundation of China (Nos. 61976181, 11931015, 61762020), Fok Ying-Tong Education Foundation, China (No. 171105), Key Technology Research and Development Program of Science and Technology Scientific and Technological Innovation Team of Shaanxi Province (No. 2020TD-013) and National Municipal Training Program of Innovation and Entrepreneurship for Undergraduates (No. 202010635053).

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Zhang, F., Zhu, J., Luo, Z., Wang, Z., Tao, L., Gao, C. (2021). Community Detection in Dynamic Networks: A Novel Deep Learning Method. In: Qiu, H., Zhang, C., Fei, Z., Qiu, M., Kung, SY. (eds) Knowledge Science, Engineering and Management. KSEM 2021. Lecture Notes in Computer Science(), vol 12815. Springer, Cham. https://doi.org/10.1007/978-3-030-82136-4_10

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  • DOI: https://doi.org/10.1007/978-3-030-82136-4_10

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