Dynamic Network Community Detection with Coherent Neighborhood Propinquity

The community structure detection in static networks often ignores the dynamic nature of the network and it is difficult to identify the evolution of community structure in dynamic networks. The community structure will converge or split as the nodes and edges change. Understanding the evolution of communities over time is an important issue in the study of social networks. Based on the characteristics of dynamic networks, this paper analyzed the influence of variables in dynamic networks structure. We proposed an Incremental algorithm with Coherent Neighborhood Propinquity in dynamic networks. The algorithm considered the direct and indirect effects of changing nodes in their previous communities. We also considered the coherent neighborhood propinquity and improved the influence range of variable nodes. Comparing with the traditional algorithms, the experimental results showed that the proposed algorithm has better performance and less running time.