Detecting the optimal number of communities in complex networks
  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 $\Omega(c)$ is proposed to detect the optimal number of communities. In general, when $\Omega(c)$ reaches its local maximum, especially the first one, the corresponding number of communities \emph{c} is likely to be optimal in community detection. Moreover, the statistic $\Omega(c)$ 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 $\Omega(c)$ is effective in both artificial and real world networks. 
Zhifang Li, Yanqing Hu, Beishan Xu, Zengru Di, Ying Fan
physics.soc-ph cs.SI stat.AP