DIMACS TR: 2005-28
A Framework for Analysis of Dynamic Social Networks
Authors: Tanya Y. Berger-Wolf and Jared Saia
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.
Paper Available at:
ftp://dimacs.rutgers.edu/pub/dimacs/TechnicalReports/TechReports/2005/2005-28.ps.gz
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