Operations Research, Systems Engineering and Industrial Engineering Commons^™

Robust Influence Maximization, Meghna Lowalekar, Pradeep Varakantham, Akshat Kumar

Research Collection School Of Computing and Information Systems

Influence Maximization is the problem of finding a fixed size set of nodes, which will maximize the expected number of influenced nodes in a social network. The number of influenced nodes is dependent on the influence strength of edges that can be very noisy. The noise in the influence strengths can be modeled using a random noise or adversarial noise model. It has been shown that all random processes that independently affect edges of the graph can be absorbed into the activation probabilities themselves and hence random noise can be captured within the independent cascade model. On the other hand, …