Steering Social Activity: A Stochastic Optimal Control Point Of View
Steering Social Activity: A Stochastic Optimal Control Point Of View
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发表时间：
2018-02
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通讯作者：
Ali Zarezade;A. De;U. Upadhyay;H. Rabiee;M. Gomez-Rodriguez
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作者：
Ali Zarezade;A. De;U. Upadhyay;H. Rabiee;M. Gomez-Rodriguez
用户对在线社交网络的参与度很大程度上取决于相应平台中的社交活动水平，即用户进行的在线操作的数量，例如发帖、分享或回复。我们可以设计数据驱动的算法来增加社交活动吗？在用户级别，此类算法可以通过帮助用户决定何时采取更可能被同伴注意到的操作来增加活动。在网络层面，他们可以通过激励一些有影响力的用户采取更多行动来增加活动，这反过来又会触发其他用户的额外行动。在本文中，我们使用标记时间点过程的框架对社会活动进行建模，使用带有跳跃的随机微分方程（SDE）导出这些过程的替代表示，并利用这种替代表示，开发两种有效的在线算法，并提供可证明的保证来引导用户和网络层面的社交活动。在此过程中，我们在跳跃 SDE 的最优控制和双随机标记时间点过程之间建立了以前未探索过的联系，这是独立的兴趣。最后，我们对从 Twitter 收集的合成数据和真实数据进行了实验，结果表明我们的算法始终比现有技术更有效地引导社交活动。
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.