A Bayesian Regression Framework utilizing a Multiplex Graph Predictor

Oregon Convention Center 

We present an innovative regression framework featuring a scalar outcome and a multiplex graph as the predictor, where each layer of the graph captures interactions among a shared set of nodes. Popular regression methods utilizing multiplex graph predictors often face limitations in effectively harnessing information within and across network layers, leading to compromised inference and predictive accuracy, especially in scenarios with limited sample sizes. To overcome these challenges, our method models an edge coefficient in each layer as a bilinear interaction between the latent effects associated with the two connected nodes. Additionally, it employs a variable selection framework on node-specific latent effects from all layers to identify influential nodes linked to the outcome. Crucially, the proposed framework is computationally efficient and provides uncertainty in node identification, regression coefficient estimation, and binary outcome prediction. Simulation studies demonstrate the superior inferential and predictive performance of the proposed approach.