Improving Community Detection in Time-Evolving Networks Through Clustering Fusion

Traditional community detection algorithms are easily interfered by noises and outliers. Therefore, we propose to leverage a clustering fusion method to improve the results of community detection. Usually, there are two issues in clustering ensembles: how to generate efficient diversified cluster members, and how to ensembles the results of all members. Specifically: (1) considering the time evolving characteristic of real world networks, we propose to generate clustering members based on the snapshot of networks, where the split based clustering algorithms are performed; (2) considering the difference in the distribution of the cluster centers in each clustering member and the actual distribution, we ensemble the results based on a maximum likelihood method. Moreover, we conduct experiments to show that our method can discover high quality communities.