🤖 AI Summary
Existing approaches to jointly modeling multi-view graph structures and covariates often neglect inter-view dependencies or inadequately integrate covariate information, limiting inference accuracy. This work proposes the first unified hierarchical Bayesian framework that simultaneously models multi-view graphs—defined over a shared node set and accommodating binary or continuous edge weights—together with vector-valued covariates. By designing tailored priors, the framework enables principled graph fusion and parameter estimation while providing full quantification of uncertainty. Theoretical analysis establishes asymptotic consistency of the posterior predictive density. Empirical evaluations demonstrate superior performance over state-of-the-art methods in simulations, and a successful application in neuroscience reveals meaningful associations between brain functional connectivity during cognitive tasks and phenotypic measures.
📝 Abstract
Joint modeling of multiview graphs with a common set of nodes between views and auxiliary predictors is an essential, yet less explored, area in statistical methodology. Traditional approaches often treat graphs in different views as independent or fail to adequately incorporate predictors, potentially missing complex dependencies within and across graph views and leading to reduced inferential accuracy. Motivated by such methodological shortcomings, we introduce an integrative Bayesian approach for joint learning of a multiview graph with vector-valued predictors. Our modeling framework assumes a common set of nodes for each graph view while allowing for diverse interconnections or edge weights between nodes across graph views, accommodating both binary and continuous valued edge weights. By adopting a hierarchical Bayesian modeling approach, our framework seamlessly integrates information from diverse graphs through carefully designed prior distributions on model parameters. This approach enables the estimation of crucial model parameters defining the relationship between these graph views and predictors, as well as offers predictive inference of the graph views. Crucially, the approach provides uncertainty quantification in all such inferences. Theoretical analysis establishes that the posterior predictive density for our model asymptotically converges to the true data-generating density, under mild assumptions on the true data-generating density and the growth of the number of graph nodes relative to the sample size. Simulation studies validate the inferential advantages of our approach over predictor-dependent tensor learning and independent learning of different graph views with predictors. We further illustrate model utility by analyzing functional connectivity graphs in neuroscience under cognitive control tasks, relating task-related brain connectivity with phenotypic measures.