Coevolving Latent Space Network with Attractors Models for Polarization
Abstract: I will present a broadly applicable class of coevolving latent space network with attractors (CLSNA) models, where nodes represent individual social actors assumed to lie in an unknown latent space, edges represent the presence of a specified interaction between actors, and attractors are added in the latent level to capture the notion of attractive and repulsive forces. We apply the CLSNA models to understand the dynamics of partisan polarization on social media, where we expect US Republicans and Democrats to increasingly interact with their own party and disengage with the opposing party. Our analysis confirms the existence of partisan polarization in social media interactions among both political elites and the public. Moreover, while attractive partisanship is the driving force of interactions across the full periods of study for both the public and Democratic elites, repulsive partisanship has come to dominate Republican elites' interactions since the run-up to the 2016 presidential election. This is joint work with Xiaojing Zhu, Hancong Pan, Kostas Spiliopoulos, Dylan Walker, Dino Christenson, and Cantay Caliskan.
Bio: Eric Kolaczyk is a professor in McGill University’s Department of Mathematics and Statistics, and the inaugural director of the McGill Computational and Data Systems Initiative (CDSI). His research is focused on how statistical and machine learning theory and methods can support human endeavours enabled by computing and engineered systems, frequently from a network-based perspective of systems science. He collaborates regularly on problems in computational biology, computational neuroscience and, most recently, AI-assisted chemistry and materials science. He has published over one hundred articles, including several books on the topic of network analysis.
As an associate editor, Kolaczyk has served on the boards of JASA and JRSS-B in statistics, IEEE IP and TNSE in engineering, and SIMODS in mathematics. He formerly served as co-chair of the U.S. National Academies of Sciences, Medicine, and Engineering Roundtable on Data Science Education. He is an elected fellow of the AAAS, ASA and IMS, an elected senior member of IEEE, and an elected member of the ISI.
Host: Robert Lunde