Least cost rumor influence minimization in multiplex social networks

This paper deals with the issue of rumors propagation in online social networks (OSNs) that are connected through overlapping users, named multiplex OSNs. We consider a strategy to initiate an anti-rumor campaign to raise the awareness of individuals and prevent the adoption of the rumor for further limiting its influence. Therefore, we introduce the Least Cost Anti-rumor Campaign (LCAC) problem to minimize the influence of the rumor. The proposed problem defines the minimum number of users to initiate this campaign, which reaches a large number of overlapping users to increase the awareness of individuals across networks. Due to the NP-hardness of LCAC problem, we prove that its objective function is submodular and monotone. Then, we introduce a greedy algorithm for LCAC problem that guarantees an approximation within (1-1/e) of the optimal solution. Finally, experiments on real-world and synthetics multiplex networks are conducted to investigate the effect of the number of the overlapping users as well as the networks structure topology. The results provide evidence about the efficacy of the proposed algorithm to limit the spread of a rumor.