Community detection using global and local structural information
Hai-Long Yan Ju Xiang Xiao-Yu Zhang Jun-Feng Fan Fang Chane Gen-Yi Fu Er-Min Guo Xin-Guang Hu Ke Hu Ru-Min Wang
Click here to view fulltext PDF
Community detection is of considerable importance for understanding both the structure and function of complex networks. In this paper, we introduced the general procedure of the community detection algorithms using global and local structural information, where the edge betweenness and the local similarity measures respectively based on local random walk dynamics and local cyclic structures were used. The algorithms were tested on artificial and real-world networks. The results clearly show that all the algorithms have excellent performance in the tests and the local similarity measure based on local random walk dynamics is superior to that based on local cyclic structures.
Hai-Long Yan1 Ju Xiang2 Xiao-Yu Zhang2 Jun-Feng Fan2 Fang Chane2 Gen-Yi Fu2 Er-Min Guo2 Xin-Guang Hu3 Ke Hu4 Ru-Min Wang1
Volume 97, 2023
Continuous Article Publishing mode
Click here for Editorial Note on CAP Mode