Multistage Campaigning in Social Networks

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.

References in corpus (5)

• Learning Granger Causality for Hawkes Processes
• Learning and Forecasting Opinion Dynamics in Social Networks
• Smart broadcasting: Do you want to be seen?
• A Stochastic Differential Equation Framework for Guiding Online User Activities in Closed Loop
• Influence Prediction for Continuous-Time Information Propagation on Networks

Cited by in corpus (10)

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• Spatio-Temporal Point Process for Multiple Object Tracking
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• Joint Modeling of Event Sequence and Time Series with Attentional Twin Recurrent Neural Networks
• Identifying Cost-effective Debunkers for Multi-stage Fake News Mitigation Campaigns
• Correlated Cascades: Compete or Cooperate
• Detecting weak changes in dynamic events over networks
• Network Diffusions via Neural Mean-Field Dynamics
• A Jump Stochastic Differential Equation Approach for Influence Prediction on Information Propagation Networks
• Toward Maximizing the Visibility of Content in Social Media Brand Pages: A Temporal Analysis