arXiv subjects

Jose Rodriguez-Acosta

Publications and source records attributed to Jose Rodriguez-Acosta.

3 recordsLinked to original sources

arXiv2026

Multiview Graph Fusion with Covariates

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.

stat.ME↗

arXiv2026

Integrative Predictor-Dependent Learning of Network Data and Spatially Correlated Nodal Attributes for Multimodal Brain Imaging in Aging

This article introduces a predictor-dependent joint modeling framework for network data obtained from multiple subjects over a shared set of nodes with spatial co-ordinates and spatially correlated nodal attributes. The framework is highly flexible, allowing concurrent inference on nodes significantly associated with a predictor, spatial associations of nodal attributes and the regression relationship between a predictor and edge connecting a pair of nodes or a specific nodal attribute. Empirical results indicate a superior performance of the proposed approach due to accounting for network structure and spatial correlation in the data simultaneously. The methodology analyzes multimodal brain imaging data collected first-hand in the coauthor's Lifespan Cognitive and Motor Neuroimaging Laboratory, with a focus on integrating structural and functional information. It examines brain connectivity, represented as a connectome network across regions of interest (ROIs) derived from functional magnetic resonance imaging (fMRI), while also incorporating ROI-specific attributes obtained from structural MRI data, for each subject. Subject-specific aging-related features and spatial locations of ROIs are incorporated in the analysis. This framework facilitates robust inference on the associations between predictors and brain connectivity patterns, the spatial relationships among ROI-specific attributes, and the regression relationships involving edges or ROI-specific attributes with aging-related predictors. By integrating these diverse data sources, the approach provides a deeper understanding of the complex interplay between brain structure, function, aging-related changes, and external predictors. As a model-based Bayesian approach, it provides uncertainty quantification for all inferences, offering robust and reliable results, particularly in scenarios with limited sample size.

stat.AP↗

arXiv2026

Integrative Learning of Dynamically Evolving Multiplex Graphs and Nodal Attributes Using Neural Network Gaussian Processes with an Application to Dynamic Terrorism Graphs

Exploring the dynamic co-evolution of multiplex graphs and nodal attributes is a compelling question in criminal and terrorism networks. This article is motivated by the study of dynamically evolving interactions among prominent terrorist organizations, considering various organizational attributes like size, ideology, leadership, and operational capacity. Statistically principled integration of multiplex graphs with nodal attributes is significantly challenging due to the need to leverage shared information within and across layers, account for uncertainty in predicting unobserved links, and capture temporal evolution of node attributes. These difficulties increase when layers are partially observed, as in terrorism networks where connections are deliberately hidden to obscure key relationships. To address these challenges, we present a principled methodological framework to integrate the multiplex graph layers and nodal attributes. The approach employs time-varying stochastic latent factor models, leveraging shared latent factors to capture graph structure and its co-evolution with node attributes. Latent factors are modeled using Gaussian processes with an infinitely wide deep neural network-based covariance function, termed neural network Gaussian processes (NN-GP). The NN-GP framework on latent factors exploits the predictive power of Bayesian deep neural network architecture while propagating uncertainty for reliability. Simulation studies highlight superior performance of the proposed approach in achieving inferential objectives. The approach, termed as dynamic joint learner, enables predictive inference (with uncertainty) of diverse unobserved dynamic relationships among prominent terrorist organizations and their organization-specific attributes, as well as clustering behavior in terms of friend-and-foe relationships, which could be informative in counter-terrorism research.

stat.AP↗