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Multistage Campaigning in Social Networks
Mehrdad Farajtabar · Xiaojing Ye · Sahar Harati · Le Song · Hongyuan Zha

Wed Dec 07 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #151

We consider control problems for 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.

Author Information

Mehrdad Farajtabar (Georgia Tech)
Xiaojing Ye (Georgia State University)
Sahar Harati (Emory University)
Le Song (Georgia Institute of Technology)
Hongyuan Zha (Georgia Institute of Technology)

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