文献类型：期刊文献

英文题名：A community partitioning algorithm based on network enhancement

作者：Hu, Junjie[1];Wang, Zhanquan[1];Chen, Jiequan[1];Dai, Yonghui[2]

机构：[1]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China;[2]Shanghai Univ Int Business & Econ, Management Sch, Shanghai, Peoples R China

年份：2021

卷号：33

期号：1

起止页码：42

外文期刊名：CONNECTION SCIENCE

收录：;EI(收录号：20201708553958);WOS:【SCI-EXPANDED(收录号:WOS:000527530800001)】；

基金：This work was supported by Major Project of Philosophy and Social Science Research, Ministry of Education of China [grant number 19JZD010]; project of Shanghai Philosophy and Social Sciences Plan [grant number 2018BGL023].

语种：英文

外文关键词：Community partitioning; network enhancement; graph convolution; connectivity; symmetric doubly stochastic matrix

摘要：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.