Clustering networks' heterogeneous data in defining a comprehensive closeness centrality index

Abstract

One of the most important applications of network analysis is detecting community structure, or clustering. Nearly all algorithms that are used to identify these structures use information derived from the topology of these networks, such as adjacency and distance relationships, and assume that there is only one type of relation in the network. However, in reality, there are multilayer networks, with each layer representing a particular type of relationship that contains nodes with individual characteristics that may influence the behavior of networks. This paper introduces a new, efficient spectral approach for detecting the communities in multilayer networks using the concept of hybrid clustering, which integrates multiple data sources, particularly the structure of relations and individual characteristics of nodes in a network, to improve the comprehension of the network and the clustering accuracy. Furthermore, we develop a new algorithm to define the closeness centrality measure in complex networks based on a combination of two approaches: social network analysis and traditional social science approach. We evaluate the performance of our proposed method using four benchmark datasets and a real-world network: oil global trade network. The experimental results indicated that our hybrid method is sufficiently effective at clustering using the node attributes and network structure.