Community-Based Rumor Blocking Maximization in Social Networks

Even though the widespread use of social networks brings a lot of convenience to people’s life, it also cause a lot of negative effects. The spread of misinformation in social networks would lead to public panic and even serious economic or political crisis. We study the community-based rumor blocking problem to select b seed users as protectors such that expected number of users eventually not being influenced by rumor sources is maximized, called Community-based Rumor Blocking Maximization Problem (CRBMP). We consider the community structure in the social network and solve our problem in two stages, in the first stage, we allocate budget b for all the communities with the technique of submodular function maximization on an integer lattice, which is different from most of the existing work with the submodular function over a set function. We prove that the objective function for the budget allocation problem is monotone and DR-submodular, then a greedy algorithm is devised to get a \(1-1/e\) approximation ratio; then we solve the Protector Seed Selection (PSS) problem in the second stage after we obtained the budget allocation vector for communities, we greedily choose protectors for each communities with the budget constraints to achieve the maximization of the influence of protectors. The greedy algorithm for PSS problem can achieve a \(\frac{1}{2}\)-approximation guarantee. At last, we verified the effectiveness and superiority of our algorithms on three real world datasets .