Influence Maximization Under the Non-progressive Linear Threshold Model

摘要

The linear threshold model [15] has been introduced to study the influence spreading behavior in the network, for instance, the spread of virus and innovations, and the objective of influence maximization is to choose a set of initially active nodes subject to some budget constraints, such that the expected number of active nodes over time is maximized. In this paper, we extends the classic linear threshold model to capture the non-progressive behavior, i.e. the active nodes are allowed to become inactive again. The information maximization problem under our model is proved to be NP-Hard, even for the case when the underlying network has no directed cycles. The first result of this paper is negative. In general, the objective function of the extended linear threshold model is no longer submodular, and hence the hill climbing approach that is commonly used in the existing studies is not applicable. Next, as the main result of this paper, we prove that if the underlying information network is directed acyclic, the objective function is submodular (and monotone). Therefore, in directed acyclic networks with a specified budget we can achieve 1/2-approximation on maximizing the number of active nodes over a certain period of time by a deterministic algorithm, and achieve the (1 - 1/e-approximation by a randomized algorithm.