Sociability Centrality

This procedure is based on the position of nodes in a network and would represent the social skill performance of every node. One of the problems in social network analysis $(SNA)$ is identifying the importance of nodes in the network as a function of certain measures. It seems that a centrality measure that is capable of measuring the social skill of nodes is needed. So, Sociability Centrality developed to this end, based on $TOPSIS$ (a multi criteria decision making method) and Genetic Algorithm (an optimization method). Given the proposed procedure for identifying the importance of nodes from a social skills point of view, nodes in the network are evaluated based on the $TOPSIS$ method. $TOPSIS$ considers seven centrality measures (In-Degree Centrality, Out-Degree Centrality, Closeness Centrality, Betweenness Centrality, Eigenvector Centrality and PageRank Centrality) as its criteria.On the other hand, weights of the criteria are obtained using Genetic Algorithms. Scores obtained for each node based on $TOPSIS$ (sociability centrality) have very good correlation with their corresponding social skill questionnaire scores.

Sociability Centrality incorporates three features including: nodes' topological feature, nodes' psychological and sociological features and applicability to large size networks

• Agha Mohammad Ali Kermani M., Badiee A., Aliahmadi A., Ghazanfari M., Kalantari H., 2016. Introducing a procedure for developing a novel centrality measure (Sociability Centrality) for social networks using TOPSIS method and genetic algorithm. Computers in Human Behavior, 56, pp.295-305. DOI: 10.1016/j.chb.2015.11.008