Toronto Networking Seminar

Abstract:

Finding patterns of social interaction within a population has wide-ranging applications including: disease modeling, cultural and information transmission, phylogeography, conservation, and behavioral ecology. Recently, scientists have started to model social interaction with graphs (networks). One of the intrinsic characteristics of
societies is their continual change. However, majority of the social network analysis methodologies today are essentially static in that all information about the time that social interactions take place is discarded or long time series are averaged to discern the overall or long-term strength of connections. Such approach not only may give
inaccurate or inexact information about the patterns in the data, but it prevents us from even asking questions about the temporal causes and consequences of social structures. In this paper we propose a new mathematical and computational framework that allows analysis of dynamic social networks addressing the time component explicitly. We present several algorithms that explore the social structure in this model and pose many open questions.

Bio:

Dr. Tanya Berger-Wolf is an assistant professor at the Department of Computer Science at the University of Illinois at Chicago.  Her research interests are in applications of discrete modeling and analysis techniques to various areas of computational biology, particularily computational population  biology, both human (epidemiology) and animal. Dr. Berger-Wolf has received her B.Sc. in Computer Science and Mathematics from Hebrew University (Jerusalem, Israel) in 1995 and her Ph.D. in Computer Science from University of Illinois at Urbana-Champaign in 2002. She has spent two years as a postdoctoral fellow at the University of New Mexico working in computational phylogenetics and a year at the Center fo