**Training Workshops**

March 1, 2017 – Deadline for Paper Proposals and Fellowships April 28, 2017 – Early Registration ends May 31, 2017 – Last Day to Register | 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 below before arriving at the workshops. - R is required for almost all workshops,
- RStudio is highly recommended,
- statnet is frequently required, and
- xergm is required for the advanced ERGM workshops.
- Additional workshop specific packages as listed with the descriptions below
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.Descriptive TrackIntroduction to Network Theory and Methods, Michael HeaneyIntroduction to Network Analysis in R, Bruce DesmaraisThis 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 terminology, data collection and storage, network-level measures, actor-level measures of centrality, block modeling, and community detection. Example code and data will be provided, but participants are encouraged to use their own data for the in-workshop examples.Required R packages: statnetNetwork Visualization with R, Katherine OgnyanovaThis 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 session will provide a brief overview of network formats, focusing on their structure and representation in key R packages. Attendees will also receive an introduction to major principles of graphics used in the R environment. 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.Required R packages: igraph and statnetNetwork Partitioning, Scott PaulsWe will introduce the notion of partitioning networks into meaningful subgroups, demonstrating several formulations of the idea through the examination of different techniques. Using real data sets from legislative voting, international trade, and crime reports, we will explore the effectiveness and results of various approaches. All data is publicly available and we will demonstrate algorithms using R. Techniques include linkage, k-means clustering, spectral clustering, and modularity maximization.Required R packages: psclThis 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 “Descriptive Track” workshops) is assumed and familiarity with maximum likelihood estimation will be helpful. Inferential TrackIntroduction to ERGM/TERGM, Skyler CranmerRequired files: LINKERGM/TERGM in R, Philip LeifeldThis 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 texregGeneralized Exponential Random Graph Models: Inference for Weighted Networks, James D. WilsonThe 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 gergm.Required R packages: gergmLatent Factor Models for Dynamic Networks, Shahryar Minhas and Michael WardThis workshop focuses on inferential approaches to estimating Latent Networks. We survey briefly the alternatives and focus on the newly developed AME approach, which includes additive and multiplicative aspects to capture 1st, 2nd, and 3rd order dependencies in networks, including those defined by binary, count-based, and continuous data. We present theoretical, analytical, and empirical comparisons of various estimation approaches. Participants will learn how to implement these in R, interpret the results, and create visualizations of these analyses. ``` library(devtools) devtools::install_github("s7minhas/amen", ref=“dev") ``` If you encounter any errors referencing fortran or gcc when installing the package, go here for help. |