Title:A Framework for Analysis of Dynamic Social Networks
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. Social interactions are often modeled with
networks. A key characteristics of social interactions is their
continual change. However, most past analyses of social networks are
essentially static in that all information about the time that
social interactions take place is discarded. I will present
a new mathematical and computational framework that enables
analysis of dynamic social networks and that explicitly makes use of
information about when social interactions occur. I will discuss several
algorithms for obtaining information about the structure of dynamic
social networks in this framework and pose many open questions.
The research is joint work with J. Saia (UNM), D.I.Rubenstein, S. Sundaresan,
and I. Fischoff (Princeton)
Bio:
Dr. Tanya Berger-Wolf is an assistant professor in the Department of Computer
Science at the University of Illinois at Chicago. Her research is in
applications of algorithmic and data mining techniques to population biology,
both human (epidemiology) and animal, from genetics to social interactions.
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 for Discrete Mathematics
and Theoretical Computer Science (DIMACS) doing research in computational
epidemiology.
