A community detection algorithm for dynamic networks using link clustering

A community detection algorithm for dynamic networks is proposed to overcome the limitation that the current node-based dynamic community detection algorithm is difficult to identify the stable community structure. The algorithm uses link clustering technique and gets a link graph structure of the network, and then the complex incremental information in the dynamic network such as addition and removing of nodes and edges are simplified into addition and removing of links. An improved link partition density function is proposed to process a link in the incremental information and decide whether the link should be joined into the community based on the existing community structure to get the optimal community structure. At the end, the algorithm transforms the optimal link community structure into a node-based community structure. Experimental results and comparisons with the node-based community detection algorithm show that the algorithm can get the stable community structure and the modularity and NMI can raise at least 0.19 and 0.13, respectively, and that the efficiency of the algorithm is superior to the dynamic node-based community detection algorithms.