In this talk, a Bayesian framework is proposed to cluster observations sharing a common undirected network. The clustering, denoted as Multi-center Graph Clustering (McGC), is driven by network structures and strength of edges and allows the variables in each cluster to have different profiles. Pseudo nodes designed with no expected connections with the original nodes are introduced to control false connections aiming to facilitate graph constructions. Extensive simulations demonstrate the feasibility of the proposed approach and applications of McGC to epigenetic data support its value in practice with a potential to benefit future studies in predicting disease risk at a much earlier stage of life.

Dr. Hongmei Zhang is the Bruns Endowed Professor and Division Director in Epidemiology, Biostatistics, and Environmental Health of the School of Public Health at the University of Memphis. Her research focus is on statistical methodology development in variable selection, clustering, and Bayesian networks with application to phenotypic data and genetic and epigenetic data. Her work has been mainly supported by NIH grants. She is an elected ASA Fellow and was awarded as University Research Professorship in 2021.