标题:A Deep Stochastic Model for Detecting Community in Complex Networks
作者:Fu, Jingcheng; Wu, Jianliang
作者机构:[Fu, Jingcheng; Wu, Jianliang] Shandong Univ, Sch Math, 27 Shanda Nanlu, Jinan 250100, Shandong, Peoples R China.; [Fu, Jingcheng] Washington Univ, 更多
通讯作者:Wu, JL
通讯作者地址:[Wu, JL]Shandong Univ, Sch Math, 27 Shanda Nanlu, Jinan 250100, Shandong, Peoples R China.
来源:JOURNAL OF STATISTICAL PHYSICS
出版年:2017
卷:166
期:2
页码:230-243
DOI:10.1007/s10955-016-1681-y
关键词:Complex networks; Community detecting; Non-negative matrix factorization
摘要:Discovering community structures is an important step to understanding the structure and dynamics of real-world networks in social science, biology and technology. In this paper, we develop a deep stochastic model based on non-negative matrix factorization to identify communities, in which there are two sets of parameters. One is the community membership matrix, of which the elements in a row correspond to the probabilities of the given node belongs to each of the given number of communities in our model, another is the community-community connection matrix, of which the element in the i-th row and j-th column represents the probability of there being an edge between a randomly chosen node from the i-th community and a randomly chosen node from the j-th community. The parameters can be evaluated by an efficient updating rule, and its convergence can be guaranteed. The community-community connection matrix in our model is more precise than the community-community connection matrix in traditional non-negative matrix factorization methods. Furthermore, the method called symmetric nonnegative matrix factorization, is a special case of our model. Finally, based on the experiments on both synthetic and real-world networks data, it can be demonstrated that our algorithm is highly effective in detecting communities.
收录类别:SCOPUS;SCIE
WOS核心被引频次:1
Scopus被引频次:1
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85000819260&doi=10.1007%2fs10955-016-1681-y&partnerID=40&md5=bc096703bbc625a17b7890b1c8dc1afd
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