A Deep Stochastic Model for Detecting Community in Complex Networks

详细信息    查看全文

• 作者：Jingcheng Fu ; Jianliang Wu
• 关键词：Complex networks ; Community detecting ; Non ; negative matrix factorization
• 刊名：Journal of Statistical Physics
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：166
• 期：2
• 页码：230-243
• 全文大小：
• 刊物类别：Physics and Astronomy
• 刊物主题：Statistical Physics and Dynamical Systems; Theoretical, Mathematical and Computational Physics; Physical Chemistry; Quantum Physics;
• 出版者：Springer US
• ISSN：1572-9613
• 卷排序：166

文摘

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.