Graph Node Clustering via Transitive Node Similarity

Abstract

This paper studies the problem of cluster detection in undirected graphs by using transitive node similarity methods. Well-defined semi-metric measures are proposed to compute the similarity between the nodes of the graph, and the clustering is based on the resulted similarity values. The proposed algorithm has three major steps. In the first step, which is optional, a ranking of all the nodes of the graph is performed by using application specific criteria (if any). In the second step, a specific node is selected and the similarity values from this node to all other nodes are computed and maintained into a similarity list. In the third step, from the resulted similarity list values, the first cluster is constructed from the nodes that have a sufficient similarity score. The last two steps, are repeated, until all the nodes of the graph have been clustered. This methodology was tested in real-world data sets and provides promising clustering results. The results of a representative real-word case of clustering nodes in a real road network are presented and validated both numerically and visually.