MEta Network Analysis
Description
Most random graph models including exponential random graph models and stochastic actor-oriented models are fitted on a single network (e.g., in one classroom). However, there is a great need to combine or compare random graph models fitted on multiple networks (e.g., in multiple classrooms or different types of social networks in the same classroom). This course introduces meta network analysis that can meet this need. It can account for both within- and cross-network correlations in the coefficients of random graph models. The multilevel meta network analysis can further include predictors at network or higher levels to explain the variation in the coefficients. As such, meta network analysis is very useful for studying how ecological factors affect network formation or network dynamics and for fitting random graph models on big networks or multiplex networks with different types of social ties. Examples and R code will be provided. Basic knowledge in R and exponential random graph models is preferred.
Outline (Request syllabus)
Fit random graph models on each network
Combine the results across networks through meta regressions
Univariate vs. multivariate regression
Fixed effects vs. random effects models
Multilevel meta network analysis
Instructor
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, 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 causal network analysis (e.g., instrumental variable methods, social network-based interventions, and multilevel meta network analysis), network formation (e.g., theories on status differential and differential homophily), combing peer-reports and self-reports to improve data measurement, big and text network analysis (e.g., the “blocking-bridging-stacking” method), and assessing the joint effects of network and neighborhood, etc.
Class Time
On-demand. Lecture and lab materials and recordings are available to view at your convenience.
Participants
Participants were from BI Norwegian Business School, The Chinese University of Hong Kong, The City University of New York, Colorado State University, ETH Zurich, Finnish Institute for Educational Research, Insper, Institute for Advanced Studies, Institute of Oriental Studies of the Russian Academy, George Mason University, IGBMC, The Mitchell Centre for Social Network Analysis, National Chiao Tung University Taiwan, National Research University Higher School of Economics, Purdue University, Queens College, Queen's University Belfast, Rensselaer Polytechnic Institute, San Diego State University, Tallahassee Community College, Texas Woman’s University, Tilburg University, Umea University, Sweden, University at Albany, SUNY, University of Arizona, University of Bern, University of Bristol, University of Chicago, University of Cincinnati, University of Florida, University of Jyväskylä, University of Kent, University of Miami, The University of Manchester, University of Mannheim, University of Michigan, University of Oxford, University of Sherbrooke, University of Southern California, University of Teacher Education Bern, University of Texas at Austin, University of Toronto, Vanderbilt University, etc.