# Multistage Campaigning in Social Networks

arXiv: https://arxiv.org/abs/1606.03816 · HTML: https://mlsanity.com/paper/1606.03816/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2016.

## Built on by (most cited first, 5 of 5)

- [Spatio-Temporal Point Process for Multiple Object Tracking](https://mlsanity.com/paper/2302.02444/) (2023)
- [A Variational Autoencoder for Neural Temporal Point Processes with Dynamic Latent Graphs](https://mlsanity.com/paper/2312.16083/) (2023)
- [Harnessing Network Effect for Fake News Mitigation: Selecting Debunkers via Self-Imitation Learning](https://mlsanity.com/paper/2402.03357/) (2024)
- [Influence Estimation and Maximization via Neural Mean-Field Dynamics](https://mlsanity.com/paper/2106.02608/) (2021)
- [Bandits on graphs and structures](https://mlsanity.com/paper/2605.03493/) (2026)

## Abstract (arXiv)

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.