Introducing a procedure for developing a novel centrality measure (Sociability Centrality) for social networks using TOPSIS method and genetic algorithm

Centrality is one of the most important fields of social network research. To date, some centrality measures based on topological features of nodes in social networks have been proposed in which the importance of nodes is investigated from a certain point of view. Such measures are one dimensional and thus not feasible for measuring sociological features of nodes. Given that the main basis of Social Network Analysis (SNA) is related to social issues and interactions, a novel procedure is hereby proposed for developing a new centrality measure, named Sociability Centrality, based on the TOPSIS method and Genetic Algorithm (GA). This new centrality is not only based on topological features of nodes, but also a representation of their psychological and sociological features that is calculable for large size networks (e.g. online social networks) and has high correlation with the nodes' social skill questionnaire scores. Finally, efficiency of the proposed procedure for developing sociability centrality was tested via implementation on the Abrar Dataset. Our results show that this centrality measure outperforms its existing counterparts in terms of representing the social skills of nodes in a social network.