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Steering social activity: a stochastic optimal control point of view

Zarezade, A ; Sharif University of Technology | 2018

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  1. Type of Document: Article
  2. Publisher: Microtome Publishing , 2018
  3. Abstract:
  4. User engagement in online social networking depends critically on the level of social activity in the corresponding platform-the number of online actions, such as posts, shares or replies, taken by their users. Can we design data-driven algorithms to increase social activity? At a user level, such algorithms may increase activity by helping users decide when to take an action to be more likely to be noticed by their peers. At a network level, they may increase activity by incentivizing a few influential users to take more actions, which in turn will trigger additional actions by other users. In this paper, we model social activity using the framework of marked temporal point processes, derive an alternate representation of these processes using stochastic differential equations (SDEs) with jumps and, exploiting this alternate representation, develop two efficient online algorithms with provable guarantees to steer social activity both at a user and at a network level. In doing so, we establish a previously unexplored connection between optimal control of jump SDEs and doubly stochastic marked temporal point processes, which is of independent interest. Finally, we experiment both with synthetic and real data gathered from Twitter and show that our algorithms consistently steer social activity more effectively than the state of the art. © 2018 Ali Zarezade, Abir De, Utkarsh Upadhyay, Hamid R. Rabiee, Manuel Gomez-Rodriguez
  5. Keywords:
  6. Information networks ; Marked temporal point processes ; Social networks ; Stochastic differential equations with jumps ; Stochastic optimal control ; Differential equations ; Information services ; Social networking (online) ; Stochastic control systems ; Stochastic models ; Doubly stochastic ; On-line algorithms ; Online social networkings ; Point process ; Stochastic differential equations ; Synthetic and real data ; Stochastic systems
  7. Source: Journal of Machine Learning Research ; Volume 18 , 2018 , Pages 1-35 ; 15324435 (ISSN)
  8. URL: http://www.jmlr.org/papers/v18/17-416.html