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Network community detection from the perspective of time series

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  • Wang, Dong
  • Zhao, Yi

Abstract

We present a quasi-isometric mapping to transform complex networks into time series, which enables the network distance to be strictly preserved and allows to solve the network clustering problem from the perspective of its time series. In order to reconstruct the network distance characteristics exactly, we weight the network links in several ways and then convert the weighted networks into time series via classical multidimensional scaling (CMDS). Given such a transformation framework, we utilize the criterion of relative eigenvalue gap (REG) to estimate the number of communities of a network. Further, we enunciate that the distributions of two time series from two isomorphic networks are identical. We then apply the distance-based k-means algorithm to the generated time series to detect the community structures of complex networks with success. The results of diverse simulated and real networks demonstrate the superiority of quasi-isometry-based time series in network community detection.

Suggested Citation

  • Wang, Dong & Zhao, Yi, 2019. "Network community detection from the perspective of time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 522(C), pages 205-214.
    • Handle: RePEc:eee:phsmap:v:522:y:2019:i:c:p:205-214
    DOI: 10.1016/j.physa.2019.01.028
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    References listed on IDEAS

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    1. Gao, Zhong-Ke & Cai, Qing & Yang, Yu-Xuan & Dang, Wei-Dong, 2017. "Time-dependent limited penetrable visibility graph analysis of nonstationary time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 476(C), pages 43-48.
    2. Robert Tibshirani & Guenther Walther & Trevor Hastie, 2001. "Estimating the number of clusters in a data set via the gap statistic," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 411-423.
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    Cited by:

    1. Boris Mirkin & Soroosh Shalileh, 2022. "Community Detection in Feature-Rich Networks Using Data Recovery Approach," Journal of Classification, Springer;The Classification Society, vol. 39(3), pages 432-462, November.
    2. Wang, Tao & Chen, Shanshan & Wang, Xiaoxia & Wang, Jinfang, 2020. "Label propagation algorithm based on node importance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).

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