  We consider the problem of how to optimize multi-stage campaigning over
social networks. The dynamic programming framework is employed to balance the
high present reward and large penalty on low future outcome in the presence of
extensive uncertainties. In particular, we establish theoretical foundations of
optimal campaigning over social networks where the user activities are modeled
as a multivariate Hawkes process, and we derive a time dependent linear
relation between the intensity of exogenous events and several commonly used
objective functions of campaigning. We further develop a convex dynamic
programming framework for determining the optimal intervention policy that
prescribes the required level of external drive at each stage for the desired
campaigning result. Experiments on both synthetic data and the real-world
MemeTracker dataset show that our algorithm can steer the user activities for
optimal campaigning much more accurately than baselines.
