Title: Computational Analysis of Dynamic Social Networks
Speaker: Tanya Berger-Wolf, DIMACS
Date: June 20, 2005 2:30 - 3:45 pm
Location: DIMACS Center, CoRE Bldg, Room 431, Rutgers University, Busch Campus, Piscataway, NJ
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). Analysis of network models has been applied to topics as varied as uncovering corporate scandals and comparing smallpox vaccination strategies to finding the mechanisms of altruistic behavior in fish and the key individuals in dolphin societies. 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. We will present a new mathematical and computational framework that allows analysis of dynamic social networks and to address the time component explicitly. We will present several algorithms that explore the social structure in this models and pose many open questions. We will present a few examples using the zebra data collected by the Dr. Dan Rubenstein's lab in Princeton.