Introductory Network Analysis

Description

By shifting the research focus from individuals to their connections network analysis brings both theoretical and methodological innovations. This course introduces the basic methods for collecting and analyzing network data. Selected topics include survey designs, data collection and representation, network plots, measures of individual centrality (indegree, outdegree, betweenness, and eigenvector centrality), measures at the dyadic level (reciprocity, geodistance, and geopath), at the group level (cliques and components), and at the network level (density, centralization, and transitivity), and how these measures may be used as outcomes or predictors in regression analysis. Examples and R code will be provided. This course requires no prior knowledge of network analysis, except basic knowledge of R and logistic regression. The course will also provide directions for studying more advanced topics such as the exponential random graph model for modeling network formation, meta network analysis, causal network analysis, dynamic network analysis, and network-based interventions.

Outline (Request syllabus)

• Introduction to social networks (Examples, history, and major topics)

• Network Data (Study design, data collection, and data representation)

• Basic Analysis (Various measures at node, dyad, group, and network levels)

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.