Multi-objective optimization to identify key players in large social networks

Identification of a set of key players in a given social network is of interest in many disciplines such as sociology, politics, finance, economics, etc. Although many algorithms have been proposed to identify a set of key players, each emphasizes a single objective of their interest. Consequently, the prevailing deficiency of each of these methods is that they perform well only when we consider their objective of interest as the only characteristic the set of key players should have. But in complicated real life applications, we need a set of key players which can perform well with respect to multiple objectives of interest. In this paper, we propose a new perspective for key player identification, based on optimizing multiple objectives of interest. This method allows us to compare other methods of key player identification. The sets of key players identified by this method are better when multiple objectives must be addressed. In addition we propose an algorithm to select the most suitable sets of key players when multiple choices are available. To reduce the computational complexity of the proposed approach for large networks, we propose a new sampling approach based on Degree centrality. We apply these algorithms in eventual influence limitation (EIL) problem and immunization problem and show that our multi-objective methodology outperforms previous key player identification approaches.