A divide and agglomerate algorithm for community detection in social networks

作     者：Liu, Zhiyuan Ma, Yinghong 

作者机构：Shandong Normal Univ Sch Business Jinan 250014 Shandong Peoples R China 

出 版 物：《INFORMATION SCIENCES》 (信息科学)

年 卷 期：2019年第482卷

页      面：321-333页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Science Foundation of China 

主　　题：Community detection DA algorithm Constrained AA index Attraction index 

摘      要：Communities, or clusters, are usually subgraphs of nodes densely interconnected but sparsely linked with others. The nodes with similar properties or behaviors are more likely to be in the same community, and vice versa. However, due to the complexity and diversity of networks, the accurate organization or function of communities in many real networks is often extremely difficult to be recognized. Hence, methods for community detection would have immediate impact on understanding the organizations and functions of networks. Therefore, algorithm design becomes a fundamental problem for many networks. In this paper, the local and global information are applied together to propose a divide and agglomerate (DA) algorithm for community detection in social networks. The DA algorithm achieves the result with a two-stage strategy: Dividing a network into small groups according to node pairs similarities, and merging a group with the other who has the biggest attraction for it until the community criterion is steady. The novel similarity, constrained AA index captures the local and global information ensuring the optimal communities detection. The results of experiments show that DA algorithm obtains superior community results compared with six other widely used algorithms, which indicate that DA algorithm has advantages for community detection. (C) 2019 Elsevier Inc. All rights reserved.