Analysis of Multiplex Social Networks Using Nature-Inspired Algorithms

Abstract

Many real-life social networks are having multiple types of interaction among entities; thus, this organization in networks builds a new scenario called multiplex networks. Community detection, centrality measurements are the trendy area of research in multiplex networks. Community detection means identifying the highly connected groups of nodes in the network. Centrality measures indicate evaluating the importance of a node in a given network. Here, the authors propose their methodologies to compute the eigenvector centrality of nodes to find out the most influential nodes in the network and present their study on finding communities from multiplex networks. They combine a few popular nature-inspired algorithms with multiplex social networks to do the above tasks. The authors' experiments provide a deep insight into the various properties of the multiplex network. They compare the proposed methodologies with several alternative methods and get encouraging and comparable results.