Please join the Statistical Computing Workshop as they host Adeline Lo, Associate Professor at the Department of Political Science and Glenn B. & Cleone Orr Hawkins Chair of Political Science at the University of Wisconsin-Madison.
Many networks in political and social research are bipartite, connecting two distinct node types. A common example is cosponsorship networks, where legislators are linked through the bills they support. However, most bipartite network analyses in political science rely on statistical models fitted to a “projected” unipartite network. This approach can lead to aggregation bias and an artificially high degree of clustering, invalidating the study of group roles in network formation. To address these issues, we develop a statistical model of bipartite networks theorized to arise from group interactions, extending the mixed-membership stochastic blockmodel. Our model identifies groups within each node type that exhibit common edge formation patterns and incorporates node and dyad-level covariates as predictors of group membership and observed dyadic relations. We derive an efficient computational algorithm to fit the model and apply it to cosponsorship data from the United States Senate. We show that senators who were perfectly split along party lines remained productive and pass major legislation by forming non-partisan, power-brokering coalitions that found common ground through low-stakes bills. We also find evidence of reciprocity norms and policy expertise impacting cosponsorships. An open-source software package is available for researchers to replicate these insights
Bio
Adeline Lo is Associate Professor of Political Science and Glenn B. and Cleone Orr Hawkins Chair at the University of Wisconsin–Madison, with an affiliation in Statistics. Her research examines the political and social conditions that foster or reduce conflict between groups, with a particular focus on migrant inclusion and the role of media in shaping public responses to refugees. Methodologically, she works across experimental, observational, text, and network data and develops statistical tools for applied social science research. Her work has appeared in outlets including the American Political Science Review, Comparative Political Studies, Nature Human Behavior, Political Analysis, and the Proceedings of the National Academy of Sciences. She currently serves as an Associate Editor of Political Analysis.
About the Workshop
The Statistical Computing Workshop (formerly R Workshop) is a year-long series that meets three times per quarter during the academic year. The purpose of the workshop is to learn, practice, and update cutting edge statistical programming skills as they apply to quantitative and computational social science.
Workshop meetings will feature internal or external speakers introducing a new tool, method, or research project involving statistical computing in the broadest sense. All meetings are hybrid or fully virtual.
Ariel Sowers
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