Community identification based on clustering coefficient

Researches show that numerous complex networks have clustering effect. It is an indispensable step to identify node clusters in network, namely community, in which nodes are closely related, in many applications such as identification of ringleaders in anti-criminal and anti-terrorist network, efficient storage of data in Wireless Sensor Network (WSN). At present, most of community identification methods still require the specifications of the number or the scale of community by user and still can´t handle boundary nodes. In an attempt to solve these problems, a network community identification method based on clustering coefficient is proposed. This method makes use of individual-centered theory for reference and can automatically determine the number of communities. It is shown through contrastive experiments that communities identified by this method have more reasonable size and closer structure than those obtained by other methods which are also based on the individual-centered theory. Finally, a research direction is proposed of network community identification method based on the individual-centered theory.