[UCI-Calit2] A Simple Model for Complex Networks

[Anna Lynn Spitzer]

A Simple Model for Complex Networks with Arbitrary Degree Distribution
and Clustering 

Human Sciences and Complexity Seminar
With Mark S. Handcock, University of Washington

1:30-3 p.m.
Friday, Feb. 22
Calit2 Room 3008

Event will be telecast to UCSD and UCLA. 

Abstract: Many descriptions of networks are based solely on their
computed degree distribution and clustering coefficient. Handcock
proposes a statistical model based on these characterizations. This
model generalizes models based solely on the degree distribution. He
will present alternative parameterizations of the model. Each
parameterization of the model is interpretable and tunable. He will also
present a simple Markov Chain Monte Carlo (MCMC) algorithm to generate
networks with the specified characteristics and provide an algorithm
based on MCMC to infer the network properties from network data and
develop statistical inference for the model. The model can be
generalized to include mixing based on attributes and other complex
social structure. 

Bio: Mark S. Handcock is professor and chair of statistics at the
University of Washington. He is a core faculty member of the Center for
Statistics and the Social Sciences. His work focuses on the development
of statistical models for the analysis of social network data,
epidemiology, spatial processes and demography. He received his Ph.D.
from the University of Chicago and is a principal author of the statnet
R code (http://csde.washington.edu/statnet), which focuses on
statistical modeling of network data through ergms, latent space and
latent cluster models. Descriptions of his work are available at
http://www.stat.washington.edu/handcock.

Google: The R software package statnet: software tools for the
representation, visualization, analysis and simulation of social network
data; The R software package latentnet: software to fit and evaluate
latent position and cluster models for statistical networks.

The preface, data sets, and software to implement the methods in Mark S.
Handcock and Martina Morris, 1999, Relative Distribution Methods in the
Social Sciences, are available   @http://tinyurl.com/28aal8 / Relative
Distribution website. 
Paper with Martina Morris @http://tinyurl.com/262fbn
Schedule of HSC events @http://intersci.ss.uci.edu/wiki
Human Complexity Maillist @http://tinyurl.com/24nxyd 

More information: drwhite at uci.edu