Open Access
ARTICLE
Improvement of Stochastic Competitive Learning for Social Network
Wenzheng Li1, Yijun Gu1, *
1 School of Information Technology and Cyber Security, People’s Public Security University of China, Beijing, 100038, China.
* Corresponding Author: Yijun Gu. Email: ; .
Computers, Materials & Continua 2020, 63(2), 755-768. https://doi.org/10.32604/cmc.2020.07984
Received 17 July 2019; Accepted 06 August 2019; Issue published 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 of the algorithm, and the F1 score of
the improved algorithm is 9.07% higher than that of original algorithm.
Keywords
Cite This Article
W. Li and Y. Gu, "Improvement of stochastic competitive learning for social network,"
Computers, Materials & Continua, vol. 63, no.2, pp. 755–768, 2020. https://doi.org/10.32604/cmc.2020.07984