Immune Algorithm to Suppress Rumor Propagation Based on Influence Maximization

Abstract

In order to effectively reduce the spread range of rumor information, an immune method based on two-stage influence maximization to suppress rumor propagation is proposed. Firstly, User and Clustering Influence Maximizing (UCIM) algorithm is proposed to obtain the most influence node set at the current moment in the initial stage of event evolution based on the network topology and user characteristics. Secondly, the node set is identified and classified based on the RNN rumor detection model, and Immunosuppression Strategy considering the Average Path Weights and User-Clustering (IS-APWUC) strategy is proposed for the identified rumor nodes. In this strategy, rumor node set is taken as root nodes, and neighbor nodes with weak influence are pruned to construct an effective rumor path tree. Thirdly, considering the total probability of being infected by the rumors and the comprehensive influence factors, the nodes with high influence in the propagation stage are calculated as immune nodes so as to block the spread of the rumor information. Finally, the proposed method is verified by experiments on four real-world datasets. The results show that the IS-APWUC method has a better rumor suppression effect than the similar correlation algorithms.