Workshops

Workshops

Foundations Track

1. Michael Heaney - Intro SNA Theory
2. Bruce Desmarais - Intro network analysis in R
3. Katherine Ognyanova - visualization
4. Betsy Sinclair - Network Survey Data

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 - Collecting and Mining Big Social Data (https://www.dropbox.com/s/gbo765qzd1g5rcw/polnet-workshop.pdf?dl=0)

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: Network Survey Data (Sinclair) -- 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: Collecting and Mining Big Social Data (Morstatter) -- Advanced Track

Workshop Descriptions

FOUNDATIONS TRACK

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.

Micheal Heaney's lecture slides can now be accessed here.

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 (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.

The full materials for the visualization workshop are available here.

The online version of the tutorial is here.

Network Survey Data

Betsy Sinclair, Washington University

In this course we examine networks through the lens of survey research. Network structure via survey are typically ascertained through the placement of network-specific items for a single respondent or snowball sampling. We will investigate the frontier of both strategies with an emphasis on establishing best practices in terms of measurement, inference of network effects, and open research questions. This is an empirical course and while no pre-requisites are required, we encourage students to bring a laptop and have R previously installed. Expect to work collaboratively.

ADVANCED TRACK

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.

ERGM/TERGM in R

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.

Collecting and Mining Big Social 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. The 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.

Betsy Sinclair, bsinclai@wustl.edu, @BetsySinclair1Betsy Sinclair is a Professor of Political Science at Washington University in St Louis. Her research interests are located in American politics and political methodology with an emphasis on individual political behavior. She focuses on the social foundations of participatory democracy -- the ways in which social networks influence voting, donating, choosing a candidate or identifying with a particular party. Betsy's other interests are broadly defined as those involving voting and elections and range from evaluating the consequences of different voting technologies to developing techniques to draw causal inferences from social network data.