In a social network, rumor containment is vital, as the diffusion of a rumor will bring terrible results. Precautionary measure can be used to control rumor propagation: Anticipating the spread of a rumor, one can (1) select a set of trustworthy people (TP) in the network, (2) alert the TP about the rumor, and (3) ask the TP to protect their neighbors by sending out alerts. In this paper, we study the problem of how to select the least number of TP, satisfying the requirement that the entire network is protected by the alerts that the TP send. We propose an asymmetric trust (AT) information propagation model. Under this model, we study the Least Number TP Selection (LNTS) problem, establish its NP-hardness and reformulate it as a minimum submodular cover problem. As a result, the Greedy Algorithm is a constant-factor approximation algorithm. Using real-world data, we evaluate the performance of the Greedy Algorithm, and compare it with other algorithms. Experimental results indicate that the Greedy Algorithm performs the best among its competitors.
A Bio-Inspired Defensive Rumor Confinement Strategy in Online Social Networks
