Workshops

All training workshops will take place on Thursday, June 23. Workshop attendees have two tracks from which to choose, with each having two sessions. Each track will be scheduled approximately 9 AM - 5 PM:

Workshop 1
Lunch
Workshop 2
Joint Bring Your Own Data session for all workshop attendees. Schedule available here.

Introductory Track

Introduction to Network Analysis with Bruce Desmarais, Pennsylvania State University

This workshop will introduce participants to network analysis in R. We will cover the basics of R programming for network analysis, as well as the fundamentals of network analysis. Topics will include network terminology, data collection and storage, graph-level measures, actor-level measures of centrality, block modeling, and community detection.

Network visualization with R with Katherine Ognyanova, Rutgers University (other materials here)

This workshop will cover network visualization using the R language for statistical computing (cran.r-project.org) and RStudio (rstudio.com). Participants should have some prior knowledge of R and network concepts. The session will provide a brief overview of network formats, focusing on their structure and representation in key R packages. Attendees will also receive an introduction to major principles of graphics used in the R environment.

The workshop will provide a step-by-step guide describing (through series of examples) the path from raw data to graph visualization in the igraph and Statnet frameworks. The advanced portion of the workshop will touch on dynamic visualization for longitudinal networks and combining networks with geographic maps. We will also discuss ways of converting graphs in R to interactive JavaScript/3D-based visualizations for the Web.

Inferential Track

Inferential Network Models, Part I with Skyler Cranmer, Ohio State University

This workshop introduces participants to several common techniques for inferential network analysis: the exponential random graph model (ERGM), the temporal exponential random graph model (TERGM), and the temporal network autocorrelation model (TNAM). This workshop focuses on the statistical methodology underlying these techniques and the interpretation of model results based on detailed discussions of several real-data examples. This morning workshop is the first part of the “Inferential Track” of workshops and Philip Leifeld’s afternoon workshop will pick up where this leaves off: covering the same topics with a focus on software implementation as opposed to this workshop’s focus on statistics and theory. A basic understanding of the anatomy of networks and their description (such as the material covered in the “Descriptive Track” workshops) is assumed and familiarity with maximum likelihood estimation will be helpful.

Inferential Network Models, Part II with Philip Leifeld, Eawag and University of Bern (other materials here)

In this workshop, participants will learn how to run exponential random graph models (ERGM) in R using the statnet suite of packages. Users will also learn how to assess the goodness of fit of network models and run temporal exponential random graph models (TERGM) and temporal network autocorrelation models (TNAM) using the xergm suite of packages. While the morning session (see Skyler Cranmer's “Inferential Network Models, Part I” description) is about statistical methodology, this afternoon workshop is about software. Participants should have a basic working knowledge of the statistical computing environment R (approximately at the level of http://tryr.codeschool.com). Participants should bring their own laptops with R packages statnet and xergm pre-installed. Questions about the workshop can be directed to philip.leifeld@eawag.ch.