The conference will feature two days of workshops on beginning and advanced topics in network analysis along two parallel tracks.
We recommend that attendees pre-install the software listed in italics before arriving at the workshops.
Please follow the instructions on the statnet installation page for the first three. Installing statnet in that way will also install ergm, sna, and network.
Please install the latest xergm 1.5 from Philip Leifeld's page (source and Windows and Mac binaries) for the advanced ERGM workshops.
Links to data files for individual presentations can be downloaded by clicking on the specific workshop below.
The workshop lineup is as follows:
**qgraph, RColorBrewer, extrafont, and ggplot2 also recommended.
June 17, 9:00–12:30pm
Introduction to Network Theory and Methods - Michael Heaney
Basic ERGMs - Skyler Cranmer
This workshop introduces the student to statistical inference on network data via the exponential random graph model (ERGM). We will begin by explaining the dangers of mistreating network data and then introduce the basic ERGM. We will discuss the specification, estimation, and interpretation of the model. We will also show and discuss several practical examples using the ERGM package in R.
June 17, 1:30–5:00pm
Intro to Network Analysis in R - Lorien Jasny
This workshop session will serve as a basic introduction to the importation, manipulation, and descriptive analysis of social network data within the R/statnet platform. Topics covered will include: basics of R syntax; an overview of basic R functions and data types; importation of network data into R; network data manipulation; management of metadata for complex networks; visualization of network data; calculation of network descriptives (e.g., centrality scores, graph-level indices); and use of classical network analytic techniques (e.g., blockmodeling). No prior experience with R or statnet is assumed, but attendees should have familiarity with the basic concepts of descriptive network analysis.
Longitudinal Models [TERGM/SAOM] - Philip Leifeld
The workshop on modeling dynamic networks focuses mainly on the Temporal Exponential Random Graph Model (TERGM). The TERGM is an extension of the cross-sectional Exponential Random Graph Model (ERGM) to repeatedly observed networks (= panel network data). The following topics are covered: 1) statistical and theoretical foundations of the TERGM, 2) case study examples on the use of TERGMs, 3) conceptual similarities and differences between the TERGM and the Stochastic Actor-Oriented Model (SAOM), 4) preparing data for a TERGM analysis in R, 5) specifying and estimating TERGMs using the xergm package in R, 6) specifying different kinds of temporal dependencies, 7) assessing and comparing the goodness of fit of (longitudinal) network models, 8) out-of-sample prediction, and 9) micro-level interpretation of (T)ERGMs. Basic familiarity with cross-sectional ERGMs using the R packages statnet and ergm is required for participation (as covered by the ERGM workshop in the morning session). Participants should bring their laptops with R and xergm installed.
June 17, 5:30–7:30pm
**NEW** A User's Guide to FEC Data - Navid Dianati, Derek Ruths, and David Lazer
June 18, 9:00–12:30pm
Network visualization with R - Katherine Ognyanova
Latent Space Modeling for Social Networks - Jason Morgan
This course will provide a theoretical and applied introduction to latent space models for social networks. In addition to providing an introduction to the specification and estimation of the standard cross-sectional latent space model, the course will also provide detailed discussions of model identification, diagnostics, and interpretation. More advanced models that provide for node clustering and sociality effects will also be introduced. Finally, a brief discussion of newly developed methods for analyzing longitudinal models will be provided. It is strongly recommended that workshop participants have a working familiarity with R, which will be used extensively throughout the workshop.
June 18, 1:30–5:00pm
Community Detection - Scott Pauls
Community detection in networks is a basic tool for both simplifying and de-noising data. For several standard methods for detecting communities, we will explore the background assumptions, applicability of the methods, and computational considerations. To keep the discussion from being overly theoretical, we will apply methods to running examples using empirical political science data.
Generalized Exponential Random Graph Models: Inference for Weighted Networks - James Wilson
The exponential random graph model (ERGM) is a popular and flexible tool for statistical inference with network data. However, a major limitation of the common formulation of the ERGM is that it can only be applied to networks with dichotomous edges. In this workshop, we will present a flexible distribution on weighted networks that generalizes the ERGM to networks with weighted edges. Through the use of Markov Chain Monte Carlo methodology, we show how to efficiently simulate and estimate these generalized exponential random graph models (GERGMs). Through a numerical study with both simulations and real applications, we highlight the capability of the GERGM, and show how one can use this framework to avoid degeneracy issues that commonly arise in the use of standard exponential random graphs. All applications will be illustrated using built in functions from the R package xergm.
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