A Study on Network Clustering Using Similarity Based on Node Neighborhood Sets

Abstract

SCAN is a network data clustering method that represents the structure of nodes by their neighborhood sets and extracts clusters, hubs, and noise using a similarity called structural similarity. Since structural similarity is a set similarity, the Jaccard and Dice coefficients, which are also set similarities, can be used to express similarity based on node neighborhood sets. In this paper, we examine the characteristics of clustering results based on the Jaccard coefficient and other similarities of each set. First, clustering is performed using the Jaccard coefficient and other similarities in SCAN, and the results are evaluated by ARI and Modularity. Next, we visualize the similarity of the clustering results using the multidimensional scaling. From the experimental results, it was confirmed that the Jaccard coefficient and other set similarities can extract clusters that are difficult with Modularity, and that SCAN using the Simpson coefficient in particular tends to show a high ARI.