An Improved PageRank Algorithm for Multilayer Networks

Abstract

Complex networks are suitable for modeling real world systems. A multilayer network is a complex network, where each node establishes relationships with other nodes, across the layers. Ranking of nodes in multilayer networks is considered to be a key research problem for analyzing the dynamics of networks. In general, nodes are ranked using metrics such as degree centrality, betweenness centrality, and PageRank. However, these metrics are not suitable for multilayer networks, since rank may not display the actual influence of a node. This paper proposes a novel ranking metric m-PageRank for finding influential nodes in the multilayer network. Experiments using real dataset show the benefits of proposed metric.