Wenzheng Li1, Yijun Gu1, *
CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 755-768, 2020, DOI:10.32604/cmc.2020.07984
- 01 May 2020
Abstract As an unsupervised learning method, stochastic competitive learning is
commonly used for community detection in social network analysis. Compared with the
traditional community detection algorithms, it has the advantage of realizing the timeseries community detection by simulating the community formation process. In order to
improve the accuracy and solve the problem that several parameters in stochastic
competitive learning need to be pre-set, the author improves the algorithms and realizes
improved stochastic competitive learning by particle position initialization, parameter
optimization and particle domination ability self-adaptive. The experiment result shows
that each improved method improves the accuracy More >
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