TERGM: Exponential Random Graph Models for Dynamic Network Data
Date and Time: Thursday, June 27 (8:30 am - 12:30 pm)
Room: Oak
Description
Exponential random graph models (ERGMs) are flexible statistical models for relational data that are capable of representing and identifying an extensive range of interdependencies common in networks. ERGMs are conventionally applied to cross-sectional network data. The temporal ERGM (TERGM) is a recently developed extension of ERGMs to dynamic network data. How long does it take for a tie to be reciprocated? Will a friend of a friend become a friend? Do new actors in a network exhibit preference for already popular actors? Fundamental questions of network dynamics such as these can be directly addressed within the TERGM framework. This workshop will introduce the TERGM and demonstrate its application in the free and open source R statistical software. Participants will be provided with real-world longitudinal political network data as well as R code to apply TERGMs to that data.
Instructor
Bruce received his PhD from UNC Chapel Hill in 2010 and joined UMass Amherst that year as an assistant professor in the Department of Political Science and a core faculty member in the Computational Social Science Initiative. Bruce's research focuses on the development and application of methods for the analysis of political networks. Substantive applications in his work include international security, legislative collaboration and communication networks within local government organizations.
Teaching Assistant: Luke Shimek (Indiana University)
Required equipment/software
Workshop participants will require an internet-enabled laptop to download data archives and R code during the workshop. They are also encouraged to download R and the ergm package for R ahead of time.