A community partitioning algorithm based on network enhancement. Issue 1 (2nd January 2021)

• Record Type: Journal Article
• Authors: Hu, Junjie; Wang, Zhanquan; Chen, Jiequan; Dai, Yonghui
• Is Part Of: Connection science. Volume 33:Issue 1(2021)
• Page Start: 42
• Page End: 61
• Publication Date: 2021-01-02
• Subjects: Community partitioning -- network enhancement -- graph convolution -- connectivity -- symmetric doubly stochastic matrix; Neural computers -- Periodicals; Artificial intelligence -- Periodicals; Cognitive science -- Periodicals; Connectionism -- Periodicals; 006.3
• DOI: 10.1080/09540091.2020.1753172
• Languages: English
• ISSNs: 0954-0091

Abstract

Abstract : In recent years, as an effective method to mine information from the complex network, community discovery has been widely used in social network, financial risk control and other fields. However, the existing community discovery algorithms are not effective in dealing with complex network which always contains fuzzy community structure. With the help of graph convolution, the proposed algorithm defines the connectivity between any nodes in a network and constructs the symmetric doubly stochastic matrix. Then, the algorithm enhances the network by the nonlinear transformation of the eigenvalues of the symmetric doubly stochastic matrix and makes the original fuzzy community structure become clear. Experimental results show that this method can effectively sharpen the community structure of a network and improve the effect of community partitioning.