Title: Statistical Modeling of Social Networks

Abstract:

We give an overview of statistical approaches to (social) network 
modeling and their implementation in the "statnet" suite of packages 
(http://statnet.org).

Network models are widely used to represent relational
information among interacting units and the implications of these
relations.  In studies of social networks recent emphasis has
been placed on random graph models where the nodes usually
represent individual social actors and the edges represent a
specified relationship between the actors.

The modeling of social networks is, and has been, broadly
multidisciplinary with significant contributions from the social,
natural and mathematical sciences.  This has lead to a plethora
of terminology, and network conceptualizations commensurate with
the varied objectives of network analysis.  As a primary focus of
the social sciences has been the representation of social
relations with the objective of understanding social structure,
social scientists have been central to this development.

Exponential family random graph models attempt to represent the
complex dependencies in networks in a parsimonious, tractable and
interpretable way.  A major barrier to the application of such
models has been lack of understanding of model behavior and a
sound statistical theory to evaluate model fit.  This problem has
at least three aspects: the specification of realistic models;
the algorithmic difficulties of the inferential methods; and the
assessment of the degree to which the network structure produced
by the models matches that of the data.

In this talk we review progress that has been made on networks
observed in cross-sectional or longitudinally.  We consider
issues of the sampling of networks and partially-observed
networks.  We also review latent cluster random effects models.

Biographical:

Mark S. Handcock is Professor of Statistics at the University of 
California - Los Angeles. His work is based largely on motivation from 
questions in the social sciences. Recent focus has been on the 
development of statistical models for the analysis of social network 
data, spatial processes and demography. He received his B.Sc. from the 
University of Western Australia and his Ph.D. from the University of 
Chicago. Descriptions of his work are available at Details of his work 
are available at <http://www.stat.ucla.edu/~handcock>

The "statnet" development team consists of
   David R. Hunter <dhunter@stat.psu.edu>,
   Carter T. Butts <buttsc@uci.edu>,
   Steven M. Goodreau <goodreau@u.washington.edu>,
   Pavel N. Krivitsky <pavel@stat.cmu.edu>,
   Martina Morris <morrism@u.washington.edu> and
   Mark S. Handcock <handcock@ucla.edu>