Network Analysis: Statistical Approaches

 
Instructor(s):

John Skvoretz, University of South Florida

This workshop covers advanced statistical methods for analyzing social network data, focusing on testing hypotheses about network structure (e.g. reciprocity, transitivity, and closure), the formation of ties based on attributes (e.g. homophily), and network effects on individual attributes (social influence or contagion models). It begins with statistical models for the local structure of dyads and triads, then moves to statistical models based on the assumption of dyadic independence, and then covers recent advances in statistical models that permit structured forms of dependence between dyads. Topics include random graph distributions, statistical models for local structure (dyads and triads), biased net models for complete networks and for aggregated tie count data, dyadic independence models, autocorrelation models, exponential random graph models, and stochastic models for dynamic network analysis.

The morning and afternoon sessions are coordinated so that each day presents methodological developments in the morning with afternoon computer lab sessions enabling applications to real data.

Prerequisites: The workshop assumes that participants have already taken a first course in network analysis, such as the ICPSR Summer Program workshop Network Analysis: An Introduction. It also assumes that participants have had a course in basic regression. Knowledge of logistic regression is helpful.

Fee: Members = $1700; Non-members = $3200

For registration details, click here.