We are pleased to offer 5 workshops, to be held via Zoom the week of Monday, 6/13 & on 6/21. Each workshop will be 3 hours. To attend the workshops, please register by Friday, 6/10 (register here). Once registered, you will receive email invitations to the Zoom rooms for each workshop. Please note, one registration grants access to all workshops but you do not have to attend all 5 workshops.

Don't hesitate to contact us if you have any questions or concerns (

Note: all times are in US Central Daylight Time.

Workshop Schedule (Descriptions Below)

  • Monday, June 13, 12-3pm CDT: Bomi Lee Introduction to Network Analysis in R

  • Wednesday, June 15, 12-3pm CDT: Ted Hsuan Yun Chen Introduction to Exponential Random Graph Models

  • Thursday, June 16, 11am-2pm CDT: Shahryar Minhas Introduction to Latent Factor Models for Networks

      • NOTE: This workshop starts an hour before the others!

  • Friday, June 17, 12-3pm CDT: Sarah Shugars Methods at the Intersection of Network and Text Analysis

  • Tuesday, June 21, 12-3pm CDT: Katherine (Katya) Ognyanova Advanced network visualization with R

      • PLEASE NOTE: This workshop has been postponed from its originally advertised date!

Workshop Descriptions

Title: Introduction to Network Analysis in R

Trainer: Bomi Lee (University of Kentucky)

Date & Time: Monday, June 13, 2022; 12-3pm CDT


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.

Instructor Bio: Bomi Lee is a Post-Doctoral Scholar at the Department of Political Science at the University of Kentucky 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.


Title: Introduction to Exponential Random Graph Models (ERGMs)

Trainer: Ted Hsuan Yun Chen (University of Helsinki)

Date & Time: Wednesday, June 15, 2022; 12-3pm CDT


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.

Instructor Bio: Ted Hsuan Yun Chen is a Postdoctoral Researcher jointly appointed in the Faculty of Social Sciences, University of Helsinki and the Department of Computer Science, Aalto University. His primary research agenda focuses on the social and political consequences of climate change, and efforts to stem these negative and often conflictual 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.


Title: Introduction to Latent Factor Models for Networks

Trainer: Shahryar Minhas (Michigan State University)

Date & Time: Thursday, June 16, 2022; 11am-2pm CDT

Materials: Materials for this workshop can be downloaded from and

Updated slides from the workshop here

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.

Instructor Bio: 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.


Title: Methods at the Intersection of Network and Text Analysis

Trainer: Sarah Shugars (New York University/Rutgers University)

Date & Time: Friday, June 17, 2022; 12-3pm CDT


Description: This workshop will introduce participants to approaches for analyzing text-as-networks, covering both conceptual foundations and practical applications. Particular focus will be given to the scope of researcher modeling choice and the challenges of inferring network structure(s) from text. The workshop will also include hands-on coding activities, demonstrating a range of strategies for inferring and interpreting textual network structure. No prior training in text or network methods is required. The coding elements of the workshop will be offered in Python, and participants are encouraged to follow along on their own computers. Those with basic familiarity of Python syntax and data structures will get the most out of the coding portion of the workshop, but anyone who is interested in developing their conceptual understanding of network and textual methods is welcome to attend.

Instructor Bio: Dr. Sarah Shugars is a computational social scientist, developing new methods in natural language processing, network analysis, and machine learning in order to examine political communication and questions of how communication has changed in the digital age. They are an incoming Assistant Professor at Rutgers University's School of Communication & Information and are currently a Faculty Fellow at NYU’s Center for Data Science (CDS) and an affiliated Research Fellow in the School of Media and Public Affairs at George Washington University. They received their Ph.D. from Northeastern’s Network Science program in Spring 2020.


Title: Network Visualization with R

Trainer: Katherine Ognyanova (Rutgers University)

Date & Time: Tuesday, June 21, 2022; 12-3pm CDT


Description: This workshop will cover network visualization using the R language for statistical computing ( and RStudio ( 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.

Instructor Bio: Katherine (Katya) Ognyanova is an Associate Professor at the School of Communication and Information, Rutgers University. She studies the social factors affecting political behavior, confidence in institutions, information exposure, and public opinion formation. Her methodological expertise is in computational social science, network analysis, and survey research. Ognyanova is also one of the lead researchers on The COVID States Project (, a large multi-university initiative exploring the social and political implications of COVID-19. You can visit Katya’s website at or follow her on Twitter at @Ognyanova.