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A framework for analysis of dynamic social networks

By Tanya Y. Berger-wolf

Abstract

Finding patterns of social interaction within a population has wide-ranging applications including: disease modeling, cultural and information transmission, and behavioral ecology. Social interactions are often modeled with networks. A key characteristic 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. In this paper, we propose 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

Topics: Categories and Subject Descriptors, I.6.5 Simulation and Modeling, Model Development General Terms, Algorithms, Design Keywords, dynamic social networks, algorithms, disease
Publisher: ACM Press
Year: 2006
OAI identifier: oai:CiteSeerX.psu:10.1.1.133.9599
Provided by: CiteSeerX
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