2023 Workshops & Registration

We are pleased to offer six workshops on both beginner and advanced network analysis. Participants need to register via the University of Exeter Online Store here. Registration covers entrance to all six workshops! Early-bird rates are £25 for students and £50 for non-students. Early-bird rates will end on 1st June when rates will rise to £50 for students and £100 for non-students. Zoom links and links for materials will be sent out to registered participants before the workshops.

Beginner track:

Description: This workshop will cover a comprehensive introduction to network analysis using the R programming language. Participants will be introduced to basic network concepts, network data management, and network statistics (e.g., centrality, triad) using the igraph and Statnet packages. Following some introduction slides, participants will have chances to practice using simple toy datasets and real-world political science data (e.g., alliances). The last part of the workshop will cover network configurations in signed networks (with both positive and negative ties) using the signet package. I assume that participants have no prior knowledge of network analysis and only basic familiarity with R. Bomi Lee is a Post-Doctoral Scholar at the Department of Political Science at Washington University in St. Louis and is part of a National Science Foundation-funded research program on Women as Leaders, Policy-Makers, and Symbols. Her research focuses on the interconnectedness of amity and enmity in international relations. In her dissertation, Interconnected Rivalries, she examines the interdependence among countries by focusing on the centrality and triangular relationships in interstate rivalry networks. She worked as a research consultant at the Iowa Social Science Research Center where she taught several Introduction to Network Analysis and Network Visualization workshops. 

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 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.

This workshop will cover basic statistical methods for network analysis within the R/statnet platform. The approach taken is practical rather than theoretical, with emphasis on simple, robust methods for hypothesis testing and exploratory data analysis of single and multi-network data sets. Topics include: permutation tests for marginal relationships between node or graph-level indices and covariates and when you can use standard regression methods; Monte Carlo tests for structural biases; Quadratic Assignment Procedure (QAP), network correlation, autocorrelation, and regression; baseline models and conditional uniform graph tests; and exploratory multivariate analysis of multi-network data sets. We will also cover interpreting R code in existing functions and writing your own functions. We discuss briefly how these methods relate to Exponential Random Graph models (ERGM), but the focus of this workshop is on non-ERGM statistical methods.  


Advanced track

Description: How do we explain the formation of ties on a network? Beyond actor- and dyad-level factors, network effects such as reciprocity often play a role. This workshop introduces the exponential random graph model (ERGM) that lets us statistically study how network ties are formed. The workshop is also a tutorial on how to fit these models in R. In the first part of the workshop, we will discuss the logic of using local network configurations to study social and political processes in a networked system. Next, we will look at how to conduct inferential statistical analysis of these processes using the ERGM framework. This portion of the workshop will be heavily integrated with a tutorial on how to run these models in R using the statnet suite of packages, and will be designed to allow participants to follow the tutorial on their own computers. A basic understanding of social network analysis and a working knowledge of R is assumed. Ted Hsuan Yun Chen is an Assistant Professor in the Department of Environmental Science and Policy at George Mason University. His research agenda focuses on the social and political consequences of climate change and efforts to stem these often unequal outcomes. Methodologically, he is interested in developing computational and network approaches for studying sociopolitical phenomena as complex systems. His work on network analysis has been published in outlets such as Political Analysis and Political Science Research & Methods. 

Description: This workshop will provide an introduction to latent space models in general with a particular focus on Peter Hoff’s latent factor model. We will talk about how it can be used for inference when dealing with dyadic data and how it can be used to obtain lower dimensional representations of how actors relate to one another. Both these topics will be handled in the context of applied examples from political science. Last, we’ll discuss the extent to which AME can be used for longitudinal networks and caveats to keep in mind when using it for inferential tasks. Shahryar Minhas is an Assistant Professor at Michigan State University and received his Ph.D. in Political Science from Duke University in 2016. Generally, his research program stems from his training in international relations and political methodology with a focus on how to study social systems in which the actions of actors are interdependent. His methodological focus falls under the heading of network science, but his two specific areas of interest are: how can we do inference in the presence of interdependent observations and how can we build on network properties to learn about the underlying structure of a social system. His research has appeared in the Journal of Politics, Political Analysis, Network Science, Social Networks, among other journals.