Categorization method for nodes in complex networks and its application

Abstract

Nodes are often categorized using some centrality or similarity measures in order to analyze the structure of complex networks. Sometimes a community structure is used for the node categorization. However, there are few studies that the nodes are categorized based on multiple characteristic properties which can be defined at each node such as the degree, local clustering coefficient, local geodesic distance, etc. In this study, we propose a new categorization method for nodes in complex networks. First, we calculate several local characteristic properties at each node, and define the attribute vector of the node which each component corresponds to such properties. Second, the nodes are categorized by clustering multivariate data, i.e. the attribute vectors. SOM-based simple clustering method is used in this paper. Finally, one example is demonstrated to show how the proposed method works well. We also show the effectiveness of our categorization method to analysis of simulations on networks.