Multistage Campaigning in Social Networks

TL;DR

This paper develops a dynamic programming approach to optimize multi-stage social network campaigns by modeling user activity as a multivariate Hawkes process, enabling more accurate intervention strategies.

Contribution

It introduces a theoretical framework linking exogenous event intensity to campaign objectives and proposes a convex dynamic programming method for optimal intervention policy design.

Findings

The proposed algorithm outperforms baselines in synthetic and real-world data.

Theoretical foundations connect campaign objectives with user activity intensities.

Effective multi-stage campaigning strategies are achieved through the developed framework.

Abstract

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.