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.

Bio: Manuel Gomez Rodriguez is a tenure-track research group leader at
Max Planck Institute for Software Systems. Manuel develops machine
learning and large-scale data mining methods for the analysis and
modeling of large real-world networks and processes that take place
over them. He is particularly interested in problems arising in the
Web and social media and has received several recognitions for his
research, including an Outstanding Paper Award at NIPS'13 and a Best
Research Paper Honorable Mention at KDD'10. Manuel holds a PhD in
Electrical Engineering from Stanford University and a BS in Electrical
Engineering from Carlos III University in Madrid (Spain).