2011/03/30 by Zhifang Li, Yanqing Hu, Beishan Xu +2
Computer Science · Mathematics · Physics and Astronomy · Psychology · #Complex Network Analysis Techniques #Mental Health Research Topics #Opinion Dynamics and Social Influence #cs.SI #physics.soc-ph #stat.AP
paper · pdf · doi:10.1016/j.physa.2011.06.023
8 pages, 6 figs
arxiv created 2011/03/30 · openalex publication_date 2011/07/20 · arxiv updated 2015/05/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
To obtain the optimal number of communities is an important problem in detecting community structure. In this paper, we extend the measurement of community detecting algorithms to find the optimal community number. Based on the normalized mutual information index, which has been used as a measure for similarity of communities, a statistic Ω(c) is proposed to detect the optimal number of communities. In general, when Ω(c) reaches its local maximum, especially the first one, the corresponding number of communities c is likely to be optimal in community detection. Moreover, the statistic Ω(c) can also measure the significance of community structures in complex networks, which has been paid more attention recently. Numerical and empirical results show that the index Ω(c) is effective in both artificial and real world networks.