A network motif based approach for classifying online social networks

Abstract

Complex networks facilitate the understanding of natural and man-made processes and are classified based on the concepts they model: biological, technological, social or semantic. The relevant subgraphs in these networks, called network motifs, are demonstrated to show core aspects of network functionality. They are used to classify complex networks based on that functionality. We propose a novel approach of classifying complex networks based on their topological aspects using motifs. We define the classifiers for regular, random, small-world and scale-free topologies, as well as apply this classification on empirical networks. The study brings a new perspective on how we can classify and differentiate online social networks like Facebook, Twitter and Google Plus based on the distribution of network motifs over the fundamental network topology classes.