Important Dates:

Call for Proposals
December 1, 2017

Deadline to Submit Proposals and Apply for Funding
February 15, 2018

Notification of Acceptance
March 15, 2018

(Early Bird) Deadline to accept conference role and/or funding
April 1, 2018 

Conference registration FINAL Deadline
April 30, 2018


Foundations Track
  1. Michael Heaney - Intro SNA Theory
  2. Bruce Demarais - Intro network analysis in R
  3. Katherine Ognyanova - visualization
  4. Meredith Rolfe - Collecting Network Data with Surveys
Advanced Track
  1. Skyler Cranmer - Intro to ERGM/TERGM
  2. Philip Liefeld —ERGM/TERM in R
  3. Bailey Fosdick - Latent Factor Models
  4. Fred Morstatter - Big Data
Foundations Track Schedule
June 6
9am - 12pm Intro to SNA Theory (Heaney) -- Foundations Track
1pm - 4pm Intro to network analysis in R (Desmarais) -- Foundations Track

June 7
9am - 12pm Visualization (Ognyanova) -- Foundations Track
1pm - 4pm Collecting Network Data with Surveys (Rolfe) -- Foundations Track

Advanced Track Schedule
June 6
9am - 12pm Intro to ERGM (Cranmer) -- Advanced Track
1pm - 4pm ERGM/TERGM in R (Liefeld) -- Advanced Track

June 7
9am - 12pm Latent Factor Models (Fosdick) -- Advanced Track
1pm - 4pm Big Data (Morstatter) -- Advanced Track

Workshop Descriptions

Introduction to Social Network Analysis and Theory
Michael Heaney, University of Michigan
This workshop introduces the questions, concepts, theories, methods, and important findings of social network analysis for scholars that have no prior training in this field.  It begins by discussing the logic of relational thinking and motivations for the study of networks, including the identification of appropriate research questions and fertile areas for research.  The collection of network data using various methodologies is considered, such as surveys, interviews, experiments, archival analysis, and ethnography.  It discusses the dilemmas of boundary specification, the trade-offs of ego-network and whole-network approaches, and the challenges of asking network questions in surveys.  It presents definitions of key objects (e.g., links, nodes, modes, components, paths, triads), one-mode versus multi-modal analysis, and network statistics (e.g., degree, degree distribution, indegree, outdegree, density, centrality, centralization).  It surveys the major theories in network analysis (e.g., balance, embeddedness, resource dependence, brokerage, status, homophily, multiplexity, structural holes, preferential attachment, and small world theory), as well as recent findings from studies of political behavior, legislatures, parties, law, advocacy, trade, and international conflict and cooperation.

Introduction to Network Analysis in R
Bruce A. Desmarais, Pennsylvania State University
This workshop will introduce participants to network analysis using the R statistical programming language. We will cover the basics of R programming for network analysis, as well as the fundamentals of network analysis. Topics will include network analytic terminology, network data collection and storage, network-level measures of global structure, actor/node-level measures of centrality, community detection, and basic network simulation methods. Example code and data will be provided online prior to the workshop.

Network Visualization with R
Katherine Ognyanova, Rutgers University
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.

Introduction to ERGM and TRGM
Skyler Cranmer, The 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 “Foundations Track” workshops) is assumed and familiarity with maximum likelihood estimation will be helpful. 

Philip Leifeld, University of Glasgow
This workshop will introduce the specification, estimation, goodness-of-fit assessment, and micro-level interpretation of ERGMs and TERGMs in R using the packages statnet and xergm. Statnet is the most popular software for the estimation of ERGMs. Xergm implements extensions of ERGMs, such as the TERGM, and provides tools for post-estimation analysis for both models. Participants are encouraged to follow the software tutorial on their own laptops and are expected to have current versions of the software pre-installed on their computers in this case. Participation in the introductory ERGM/TERGM workshop in the previous session is highly recommended. Required R packages: xergm and texreg.
Dr. Leifeld’s git repository can be found here.

Latent Factor Models
Bailey Fosdick
, Colorado State University
Latent space models are a powerful class of models that represent residual variation in a network by mapping nodes to locations in an underlying social space, such that nodes nearby one another have a high probability of being linked in the network.  In this workshop, we will introduce latent space models, compare them to latent class network models, such as the stochastic blockmodel, and discuss hypothesis testing to evaluate whether there is a relationship between the inferred latent space and node-level covariates.

Big Data
Fred Morstatter, University of Southern California
In this workshop we will provide the attendee with the tools and knowledge to collect, maintain, and analyze big social media data. Te course will begin with a foundation on the wealth and diversity of information that is available in social media. It will then proceed to teach the student how to collect their own. Data collection will be covered both from the perspective of commercial, off-the-shelf tools as well as custom code that can be tuned to address custom research tasks. We will conclude with a discussion of the limitations and pitfalls that accompany social media research.

Workshop Instructors

Skyler Cranmer,, @SkylerCranmer
Skyler Cranmer’s research focuses on network science, particularly the role of topology in modeling complex networks and forecasting their evolution. His goal in these efforts is to develop network-based theories, innovative statistical methods for network analysis, and policy-relevant predictions. His areas of application are eclectic, ranging from international politics to neuroscience.

Bruce Desmarais,, @brucedesmarais
Bruce Desmarais is an Associate Professor of Political Science and an Affiliate of the Institute for Cyber Science at Penn State University. His
research is focused on methodological development and applications that further our understanding of the complex interdependence that underlies politics, policymaking, and public administration. Methodologically, Bruce focuses on methods for modeling networks, analyzing dynamics on networks, and experiments on networks. Primary application areas of interest to Bruce include public policy diffusion, campaign finance, legislative networks, and internal government communication networks.

Bailey Fosdick,, @baileyfosdick
Dr. Bailey Fosdick is an Assistant Professor in the Department of Statistics at Colorado State University. She earned her PhD at the University of Washington in 2013 and spent a year as a Postdoctoral Fellow at the Statistical and Applied Mathematical Sciences Institute. Dr. Fosdick’s research
primarily focuses on developing methodology for the statistical analysis of social networks, motivated by pressing questions in population ecology, public health, political science, and sociology. Working closely with researchers in these fields, Dr. Fosdick focuses on constructing realistic network models, which are theoretically justified, leverage domain knowledge, and decompose network dependencies into interpretable quantities.

Michael Heaney,, @michaeltheaney
Michael T. Heaney is Assistant Professor of Organizational Studies and Political Science at the University of Michigan.  He holds a Ph.D. in Political Science and Public Policy from the University of Chicago.  Along with Fabio Rojas, he is author of Party in the Street: The Antiwar Movement and
the Democratic Party after 9/11 (Cambridge University Press, 2015).  Along with Melody Shemtov and Marco Roldán, he is the creator, writer, and producer of a documentary film, The Activists: War, Peace, and Politics in the Streets (Bullfrog Films, 2017).  His scholarly articles appear in journals such as Social Networks, the American Political Science Review, the American Journal of Sociology, the Journal of Politics, and Perspectives on Politics.

Philip Leifeld is an associate professor in research methods at the University of Glasgow, where he is also the Director of Graduate Training in the Graduate School of Social Sciences. His research interests focus on social and political networks, methodology, and public and environmental policy processes. His work has 
appeared in the American Journal of Political Science, the Journal of Politics, and the Journal of Statistical Software, among others. Philip's software development 
page can be found here

Fred Morstatter,, @fredmorstatter
Fred Morstatter is interested in social media mining and machine learning.  His research focuses on finding and removing biases that can skew research results from big social data. Among his publications are an ICWSM paper that investigates the representativeness of Twitter's Streaming API, two WWW papers that identify
periods of bias automatically in streaming Twitter data, 2 KDD papers, and a book: Twitter Data Analytics. He won the World Wide Web conference's Best Poster Award in 2016. Since joining ISI, he has built systems that enable people to make better forecasts with the help of machine models. He is also interested in applying machine learning techniques to address problems in social media data, such as automatic identification of misinformation and bots.

Katherine Ognyanova,, @ognyanova
Katherine (Katya) Ognyanova is an Assistant Professor at the School of Communication and Information, Rutgers University. She does work in the areas of computational social science and network analysis. Her research has a broad focus on technology and social influence in the context of civic and political behavior,
news and information seeking. You can visit Katya’s website at or follow her on Twitter at @Ognyanova.