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Shaping Social Activity by Incentivizing Users
Mehrdad Farajtabar · Nan Du · Manuel Gomez Rodriguez · Isabel Valera · Hongyuan Zha · Le Song

Tue Dec 09 04:00 PM -- 08:59 PM (PST) @ Level 2, room 210D

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.

Author Information

Mehrdad Farajtabar (Georgia Tech)
Nan Du (Georgia Tech)
Manuel Gomez Rodriguez (Max Planck Institute for Software Systems)
Isabel Valera (UC3M)
Hongyuan Zha (Georgia Tech)
Le Song (Georgia Institute of Technology)

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