Influence maximization with partial feedback

The goal of Influence maximization (IM) is to select a set of most influential users in a social network subject to a budget constraint. In this work, we propose to study the adaptive IM problem under partial-feedback model. Our main contribution in this paper is to introduce a novel adaptive policy with bounded approximation ratio. One nice feature of our policy is that we can balance the delay and performance tradeoff by adjusting the value of a controlling parameter.