Title: Once upon a graph: How to get from now to then in massive networks

Speaker: Jennifer Tour Chayes
(Microsoft Research)

Date: August 12
Time: 19:00 - 20:00
Room: Ballroom C

Chair: L. Pamela Cook

Abstract: Networks are increasingly important in many aspects of our world: physical networks like transportation networks, utility networks and the Internet, online information networks like the WWW, online social networks like Facebook and Twitter, epidemiological networks for global disease transmission, genomic and protein networks in computational biology, and many more. How do we model and learn these networks? In contrast to conventional machine learning problems, where we have many independent samples, it is often the case for these networks that we can get only one independent sample. How do we use a single snapshot today to learn a model for the network, and therefore be able to estimate the properties of the network now, and to predict a similar, but larger network in the future? In the case of relatively small or moderately sized networks, it’s appropriate to model the network parametrically, and attempt to learn these parameters. For massive networks, a non-parametric representation is more appropriate. Here I show how to use the theory of graph limits, developed over the last decade, to give consistent estimators of non-parametric models of sparse networks, and moreover how to do this in a way that protects the privacy of individuals on the network.


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