Network Analysis


Network analysis shifts the research focus from individual units to their connections and so brings both theoretical and methodological innovations. Interest in network analysis has EXPLODED especially recently, due to new advances in statistical modeling and the rapid growth of network data. This course covers the major methods for collecting and analyzing 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 and comparing estimates from multiple random network models, the stochastic actor‐oriented model for analyzing network dynamics and network effects, and social network-based interventions. The seminar will offer a good balance of major methods and diverse applications. 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 (Exponential random graph model and meta network analysis)

Day 3: Dynamic Network Analysis and Social Network-Based Interventions


Dr. Weihua An is Associate Professor (tenured) of Sociology & Quantitative Theory and Methods and associated faculty of the East Asian Studies Program, 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 (e.g., immigration, housing, education, and redistributive policy), health (e.g., peer effects on smoking, adolescent perceived risk, social network-based interventions, and pandemics), and organizations. He has published widely in both methodological and substantive journals and has also authored multiple statistical software including "fglsnet", "LARF", and "keyplayer" in R and "DIDMatch' in Stata, which have received over 130K downloads in total.  He has served or is serving on the editorial boards of American Sociological Review, Journal of Machine Learning Research, Social Science Research, Sociological Methodology, and Sociological Methods and Research and has edited several special issues at top journals including the latest Methodological Advances in Quantitative Social Science (Social Science Research 50th Anniversary Series). He is a recipient of the Faculty Teaching Award from Emory Sociology and has advised over 20 dissertations and multiple honors theses. He is an instructor for the NIDA-funded program “Training in Advanced Data Analytics and Computational Sciences” at Emory and also has extensive experience in leading methods training programs for researchers and working professionals. Dr. An has studied social network analysis for over 16 years and has published on a range of topics, including theories and methods for causal network analysis (e.g., instrumental variable methods, social network-based interventions, and multilevel meta network analysis), for analyzing network formation (e.g., theories on status differential and differential homophily), for combing peer-reports and self-reports to improve data measurement, for big and text network analysis (e.g., the “blocking-bridging-stacking” method), and for assessing the joint effects of network and neighborhood, etc. 

Class Time

Agust 9-11, 2023 (8-10:30 pm, US ET). Zoom class with recordings available.


Regular tuition: $549. Early-bird tuition (by May 10, 2023): $499. Student (or those with economic hardship) tuition (by May 10, 2023): $449.

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."