Influence maximization in social networks based on TOPSIS

Identification of influential users for news and messages propagation constitutes one of the most important topics in analysis of social networks. The success of the spreading process in these networks depends on the mechanism for selection and specification of the influential users. Beside selection of influential users, the distances between selected users should be considered in this mechanism to ensure minimum overlap and maximum coverage of a wider area of the network. Simultaneous meeting of the two objectives may be contradictory. In this paper, we propose a new approach for selection of the set of influential users using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method that seeks to present a solution by considering the above two objectives. The simulation results over real-world and artificial networks demonstrate that the set selected by the proposed approach exhibits greater spread of influence than those selected by the traditional approaches.