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Make Data Work
Sept 29–Oct 1, 2015 • New York, NY

Once upon a graph: Getting from now to then in massive networks

Jennifer Chayes (Microsoft Research)
11:30am–12:00pm Tuesday, 09/29/2015
Hardcore Data Science
Location: 1 E10/1 E11 Level: Advanced
Average rating: ***..
(3.75, 4 ratings)

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 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 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.

Photo of Jennifer Chayes

Jennifer Chayes

Microsoft Research

Jennifer Tour Chayes is Distinguished Scientist and Managing Director of Microsoft Research New England in Cambridge, Massachusetts, which she co-founded in 2008, and Microsoft Research New York City, which she co-founded in 2012. Before joining Microsoft in 1997, Chayes was for many years professor of mathematics at UCLA. Chayes is the author of over 125 academic papers and holds over 30 patents. Her research areas include phase transitions in discrete mathematics and computer science, structural and dynamical properties of self-engineered networks, graph algorithms and algorithmic game theory.

Chayes received her B.A. in biology and physics at Wesleyan University, where she graduated first in her class, and her Ph.D. in mathematical physics at Princeton. She did her postdoctoral work in the mathematics and physics departments at Harvard and Cornell. She is the recipient of a National Science Foundation Postdoctoral Fellowship, a Sloan Fellowship, and the UCLA Distinguished Teaching Award. Chayes has been the recipient of many leadership awards including the Leadership Award of Women Entrepreneurs in Science and Technology, the Women Who Lead Award, and the Women of Leadership Vision Award of the Anita Borg Institute. She has twice been a member of the Institute for Advanced Study in Princeton. Chayes is a Fellow of the American Association for the Advancement of Science, the Fields Institute, the Association for Computing Machinery, and the American Mathematical Society, and an Elected Member of the American Academy of Arts and Sciences.