On Adaptive Influence Maximization Under General Feedback Models

The classic influence maximization problem explores the strategies for deploying cascades such that the total influence is maximized, and it assumes that the seed nodes that initiate the cascades are computed prior to the diffusion process. In its adaptive version, the seed nodes are allowed to be launched in an adaptive manner after observing certain diffusion results. In this article, we provide...