Network Analysis: Advanced Topics
Instructor(s):
 Bruce Desmarais, University of Massachusetts
This is a course on inferential network analysis. We will focus on probabilistic models for network data. These models can be used to characterize uncertainty in network properties, test hypotheses about network generating processes, and simulate networks from a given distribution.
The first week will focus on exponential random graph models (ERGMs), which can be parametrized to represent complex dependence processes and the effects of exogenous covariates. The second week will cover latent space models and quadratic assignment procedure, which are both designed to account for network dependencies without formulating complex network statistics. In the third week we will cover statistical models for longitudinal networks. These will include a longitudinal extension of the ERGM, the Temporal ERGM, and the actororiented model of network dynamics (i.e., SIENA). In the >final week we will cover models for weighted networks, in which ties can assume multiple values. We will present each model mathematically, discuss published social science applications of them, and utilize the models on example datasets.
All computing will be conducted in the R statistical software. Prerequisites include the first course in Social Network Analysis at ICPSR or an equivalent introduction to network analysis, as well as familiarity with statistical modeling.
Fees: Consult the fee structure.
Tags: network, network analysis, social network, big data, system science
Course Sections
Section 1 Location: ICPSR  Ann Arbor, MI Date(s): July 20  August 14 Time: 10:00 AM  12:00 PM Instructor(s):
