Adaptive Submodular Influence Maximization with Myopic Feedback

In this paper, we study the problem of adaptive influence maximization in social networks. It has been proved that if the problem satisfies the adaptive submodularity property, then an adaptive greedy policy is guaranteed to provide an (1 − 1/e)-approximation of the optimal policy. Nevertheless, so far such a property has only been verified in the case of the full-adoption feedback model. As adaptive decision making is timecritical, we consider a more realistic feedback model, called myopic. In this direction, we introduce an alternative utility function proving that it is adaptive monotonic and adaptive submodular. It allows us to demonstrate that our myopic adaptive greedy policy provides theoretical guarantees as it retains the (1− 1/e)-approximation ratio. Furthermore, we show that the adaptive submodularity property does not hold if we allow the nodes to be deactivated randomly over time. Empirical analysis on real-world networks reveals the benefits of the myopic adaptive greedy strategy to the influence maximization problem.