# Temporal Exponential Random Graph Models (TERGMs) for dynamic networks

Workshop materials

## Prerequisites

Familiarity with R, and experience with the Statnet packages for static network analysis (ergm, network, sna). If new to network analysis with Statnet, the ERGM workshop is strongly recommended. Previous experience with the Statnet packages for descriptive analysis of temporal networks (tsna, networkDynamic and ndtv) is helpful but not required.

## Description

This workshop and tutorial provide a hands-on introduction to working with temporal network data in Statnet: from exploratory data analysis and visualization to statistical modeling with Temporal Exponential-Family Random Graph Models (TERGMs). TERGMs are a broad, flexible class of models for representing the structure and dynamics observed in temporal networks. They can be used for both estimation from and simulation of dynamic network data. The topics covered in this workshop include:

• A brief overview of exploratory data analysis with temporal network data (using the Statnet packages ‘tsna’ for descriptive statistics and ‘ndtv’ to create network movies),
• Different types of dynamic network data (network panel data, a single cross-sectional network with link duration information, and cross-sectional, egocentrically sampled network data)
• Model estimation tools for each type of data using the Statnet package tergm
• Model diagnostics in tergm, and
• Simulating dynamic networks from fitted models with tergm.

These methods can be used with both fixed and changing node sets.