A Study on Clustering Unweighted Network Data and Its Evaluation

Abstract

Most network data obtained from the real world consists only edge connection. To analyze network data using clustering, weight or dissimilarity between nodes are required. Although various weighting methods have been proposed, it has not been discussed which weighting is suitable. In this study, we used two weighting methods which are calculated from edge connection to verify the suitable weighting method to unweighted network data. One is the Euclidean distance based on adjacency matrix and the other is the diffusion kernel. Next, the k-medoids method and the Louvain method are executed to obtain network cluster partition. After that, obtained network cluster partition is evaluated by cluster validity criteria including the Modularity. The result showed that the diffusion kernel is effective.