Efficient algorithm for stochastic rumor blocking problem in social networks during safety accident period

Abstract

Rumor sources spread negative information throughout the network, which may cause unbelievable results in real society especially for social safety field. Propagating positive information from several “protector” users is an effective method for rumor blocking once the rumor is detected. Based on the probability of each user being a rumor, “protector” nodes need to be selected in order to prepare for rumor blocking. Given a social network G=(V,E,P,Q), where P(u,v) is the probability that v is activated by u after u is activated, and Q is the weight function on node set V, Qv is the probability that v will be a rumor source. Stochastic Rumor Blocking (SRB) problem is to select k nodes as “protector” such that the expected eventually influenced users by rumor is minimized. SRB will be proved to be NP-hard and the objective function is supermodular. We present a Compound Reverse Influence Set sampling method for estimation of the objective value which can be represented as a compound set function. A randomized greedy algorithm with theoretical analysis will be presented and other two different “protector” selection strategies will be proposed for comparison. Finally, we evaluate our algorithm on real world data sets and do comparison among different strategies.