Technique for Order of Preference by Similarity to Ideal Solution Based Methodology for Detecting Important Actors in Social Networks: Facebook Ego Network as a Case Study

Abstract

Detecting important actors in social networks is still a hot research topic in the complex network area. Various metrics of centrality are proposed to deal with this problem. Each metric addresses the problem from one point of view. Therefore, this paper introduces a new method to estimate the influence of nodes using multi-criteria decision-making MCDM. The proposed approach is based on the technique for order preference by similarity to an ideal solution (TOPSIS). TOPSIS aggregates centrality measures. Degree, betweenness, closeness, and eigenvector centralities are combined in this work to detect the importance of each actor. Each metric of centrality has its limitations, thus the proposed approach takes advantage of these four centralities together through TOPSIS. To illustrate the effectiveness of the proposed methodology, the Facebook ego network as an example of a social network is used for experimentation. The SI model is used to compare the spreading ability of nodes between centralities and the proposed approach on the Facebook ego network. The experimental results show that the proposed methodology can rank the important nodes more efficiently than a single measure of centrality.