Shaping Social Activity by Incentivizing Users
Shaping Social Activity by Incentivizing Users
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发表时间：
2014-08
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通讯作者：
Mehrdad Farajtabar;Nan Du;M. Gomez-Rodriguez;Isabel Valera;H. Zha;Le Song
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作者：
Mehrdad Farajtabar;Nan Du;M. Gomez-Rodriguez;Isabel Valera;H. Zha;Le Song
在线社交网络中的事件大致可以分为内生事件和外生事件。内生事件是指用户只是对网络内邻居的行为做出反应，而外生事件是指用户由于网络外部的驱动而采取行动。应该为每个用户提供多少外部驱动器，以便将网络活动引导到目标状态？在本文中，我们使用多元Hawkes过程来建模社会事件，该过程可以捕获内源性和外源性事件强度，并推导出外源性事件强度与整体网络活动之间的时间依赖线性关系。利用这种联系，我们开发了一个凸优化框架，用于确定所需的外部驱动器级别，以使网络达到所需的活动级别。我们对从Twitter收集的事件数据进行了实验，并表明我们的方法可以比其他方法更准确地引导网络的活动。
Events in an online social network can be categorized roughly into endogenous events, where users just respond to the actions of their neighbors within the network, or exogenous events, where users take actions due to drives external to the network. How much external drive should be provided to each user, such that the network activity can be steered towards a target state? In this paper, we model social events using multivariate Hawkes processes, which can capture both endogenous and exogenous event intensities, and derive a time dependent linear relation between the intensity of exogenous events and the overall network activity. Exploiting this connection, we develop a convex optimization framework for determining the required level of external drive in order for the network to reach a desired activity level. We experimented with event data gathered from Twitter, and show that our method can steer the activity of the network more accurately than alternatives.