Network Analysis

Description

Network analysis shifts the research focus from individuals to their connections and so brings both theoretical and methodological innovations. Interest in network analysis has EXPLODED recently, due to new advances in statistical modeling and the rapid growth of network data. This course covers the major methods to collect and analyze network data. Selected topics include basic network analysis (data collection, centrality, and structure), the exponential random graph model for modeling network formation, meta network analysis for combining multiple random network models, the stochastic actor‐oriented model for analyzing dynamic networks and network effects, and social network-based interventions. Case studies and R code will be provided. Knowledge of R and logistic regression is required.

Agenda (Request syllabus)

Day 1: Basic Network Analysis (Data collection, centrality, and structure)

Day 2: Network Formation and Meta Network Analysis

Day 3: Dynamic Networks, Network Effects, and Network Interventions

Instructor

Dr. Weihua An is Associate Professor of Sociology & Quantitative Theory and Methods and associated faculty of The Goizueta Business School and The Rollins School of Public Health at Emory University. He received a Ph.D. in Sociology and an A.M. in Statistics from Harvard University and was a doctoral fellow and a postdoc fellow at Harvard Kennedy School. His research advances theories and methods for network analysis and causal inference with applications to studying inequality and social policy, health, and organizations. He has published in top methodological and substantive journals and has created multiple R packages for statistical analysis. He is a recipient of the Faculty Teaching Award from Emory Sociology and has advised over 20 dissertations and multiple honors theses.

Class Time

August 7-9, 2023 (8pm-10:30pm, US ET). Zoom class with recordings available.

Registration

Regular tuition: $575. Early-bird tuition (register by June 1, 2023): $525. Alumni and student tuition (register by June 1, 2023): $475. To audit the first class for free, please register here. To register for the full course, please click the button below.

Participants and Reviews

Participants were from Catholic University of Chile (UC), Duke University, Ford Motor Company, McGill University, Oklahoma State University, Simon Fraser University, Sun Yat-sen University, University of Chicago, University of Colorado, University of Illinois at Chicago, University of Illinois at Urbana-Champaign, Vanderbilt University, among others. Below are sampled reviews from past participants.

Ashley, University of Illinois at Chicago

"I found the class to be incredibly helpful. So much ground was covered and I feel much more confident employing this type of analysis in my own research. My favorite aspects of the course were its hands-on application of methods via R labs and commentary around controversies and developments in the field of network analysis."

Kristin, Oklahoma State University

"Code and simple but effective lab examples. Excellent overall!"

Roberto, Catholic University of Chile (UC)

"I really appreciated the selection of readings, topics, and in-depth discussions we shared during the course. I also enjoyed having the chance of running codes and practicing these strategies in R."