Detecting Communities with Different Sizes for Social Network Analysis

Online community detection is essential for social network analysis. Modularity is a quality function used to measure the strength of the community structure discovered with social networks. Existing methods detect community structures through modularity analysis. However, the existing modularities are unable to identify community structures correctly when communities are very different in size, in particular, when the size of some communities is very small compared with others. To address this problem, we propose the concept of a coupling coefficient between two communities and define a new modularity, MC modularity, to evaluate the quality of the discovered community structures. This method can provide adequate measures for the quality of community structures. In addition, we develop the DC_MC algorithm to detect community structures based on the concept of MC modularity. An algorithm for shifting a group of nodes, instead of one node at a time, amongst communities also is designed to achieve optimal results. The effectiveness and efficacy of the approach we proposed have been demonstrated through a set of experiments involving real data benchmarks and synthetic data sets that are purpose-built for evaluating different types of networks