An Efficient Algorithm for Influence Maximization in Signed Social Networks

Nowadays, much attention has been devoted to the issues of social networks and social influence. Social influence examines the user's behavioral changes under the influence of their neighbors. The issue of influence maximization is to find a subset of influential nodes that can maximize propagation in the network. The selection of people is very important and is the major aim of the studies. Hence, the current study aims to investigate the maximization of influence in signed social networks since in the psychology of society, negative opinions are superior to positive ones. The criteria considered for measuring influence and methods to increase it by identifying influential people are examined. The proposed solution of this paper is based on the label propagation algorithm. The algorithms used for maximizing influence in signed social networks namely a greedy algorithm and an innovative algorithm are outlined in the second section. To implement the algorithms and simulate the transfer of users' opinions in the graph network, the independent cascade propagation model is used. The proposed algorithm shows better performance and results compared to other algorithms and has less computational overhead since it finds primary nodes by detecting dense parts and not randomly. The significant novelty of the paper lies in the heart of the accuracy and authenticity of the proposed model in maximizing influence in signed social networks.