A Scalable Framework for Modeling Competitive Diffusion in Social Networks

In: Publication

1 Aug 2010

Conference Paper, Proceedings of the 2010 IEEE International Conference on Social Computing, Symposium Section
Authors: Matthias Broecheler, Paulo Shakarian, and V.S. Subrahmanian
Direct link to paper

Abstract
Multiple phenomena often diffuse through a social network, sometimes in competition with one another. Product adoption and political elections are two examples where network diffusion is inherently competitive in nature. For example, individuals may choose to only select one product from a set of competing products (i.e. most people will need only one cell-phone provider) or can only vote for one person in a slate of political candidate (in most electoral systems). We introduce the weighted generalized annotated program (wGAP) framework for expressing competitive diffusion models. Applications are interested in the eventual results from multiple competing diffusion models (e.g. what is the likely number of sales of a given product, or how many people will support a particular candidate). We define the “most probable interpretation” (MPI) problem which technically formalizes this need. We develop algorithms to efficiently solve MPI and show experimentally that our algorithms work on graphs with millions of vertices.

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We are drowning in data - exabytes of it. My research explores technologies that can help us organize, structure, and efficiently search huge amounts of information as well as automatically deduce actionable pieces of knowledge from it. Learn more

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