A Study on Controlled Node Sized Network Clustering for Unweighted Network Data

Abstract

The Louvain method is one of the typical network clusterings. It is well-known that the Louvain method obtains better cluster partition in a short time. However, there are several network data which are not obtained better cluster partition by the Louvain method. One of the reasons for the above is that the Louvain method focuses on an only edge connection. We proposed the method which focuses on node size. The proposed method optimizes the objective function of k-medoids by solving the linear programming problem under the constraints on node size. We verified the usefulness of the proposed method in the viewpoint of calculation time and accuracy with an artificial and benchmark unweighted network datasets. The numerical examples show that the proposed method is faster and obtains better cluster partition than the Louvain method. The Euclidean distance in adjacency matrix does not obtain better cluster partition for the datasets, which consist of terminal nodes or high degree nodes.