@Article{cmc.2020.07984,
AUTHOR = {Wenzheng Li, Yijun Gu},
TITLE = {Improvement of Stochastic Competitive Learning for Social  Network},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {63},
YEAR = {2020},
NUMBER = {2},
PAGES = {755--768},
URL = {http://www.techscience.com/cmc/v63n2/38542},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2020.07984}
}