Analyzing multiple rankings of influential nodes in multiplex networks

Abstract

In many networks, different centrality indices reveal conflicting rankings of the nodes. The problem is worsened, if the same nodes occur in different but related network layers, i.e., in multiplex networks. The main concern in the analysis of multiplex networks is maintaining the inherent nature of multiple layers in the explorations. Therefore, in this paper we discuss a method combining a fuzzy operator with a visualization, that allows the exploration of a node’s centrality with respect to different network processes on different layers of the same network simultaneously. Our empirical results indicate that an airport transportation network allows for a smaller number of different behaviors than social networks in a medium sized law firm and a large sized tweet dataset.