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Activity Number: 207 - Experiments and Inference for Social Networks
Type: Invited
Date/Time: Monday, July 31, 2017 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Computing
Abstract #322266
Title: A Computational Perspective of Information Propagation in Social Networks
Author(s): Laks V.S. Lakshmanan*
Companies: University of British Columbia
Keywords: social influence ; influence maximization ; viral marketing ; optimization ; computational complexity
Abstract:

What do the spread of rumors, propagation of diseases, adoption of products, and diffusion of infection have in common? At the heart of these seemingly disparate phenomena is a network whose nodes behave like actors, receiving information from their neighbors and selectively propagating it forward to others. Study of these phenomena has given rise to a model of a probabilistic network whose links are active with some probability. Unprecedented availability of public datasets has spurred substantial interest in several computational questions over such networks. For example, how can we select a limited number of seed users, who when incentivized to adopt a product, lead to the maximum expected number of adoptions in the network through their influence? What if there is competition and/or complementarity between products being marketed? What is the role played by the owner of the network? What is the effect of advertiser budgets in these problems? I will describe recent advances in the study of these problems. While I will focus on the application of marketing, the techniques and computational framework are applicable to other phenomena and problems.


Authors who are presenting talks have a * after their name.

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