Bayesian Spillover Graphs for Dynamic Networks
This provides a tool for analyzing systemic risk and spillover effects in dynamic systems, such as financial or ecological networks, with incremental improvements in uncertainty handling.
The paper tackles the problem of learning temporal relationships and identifying critical nodes in dynamic networks by introducing Bayesian Spillover Graphs (BSG), which leverage an interpretable framework and Bayesian uncertainty quantification to achieve significant performance gains over state-of-the-art baselines in experiments.
We present Bayesian Spillover Graphs (BSG), a novel method for learning temporal relationships, identifying critical nodes, and quantifying uncertainty for multi-horizon spillover effects in a dynamic system. BSG leverages both an interpretable framework via forecast error variance decompositions (FEVD) and comprehensive uncertainty quantification via Bayesian time series models to contextualize temporal relationships in terms of systemic risk and prediction variability. Forecast horizon hyperparameter $h$ allows for learning both short-term and equilibrium state network behaviors. Experiments for identifying source and sink nodes under various graph and error specifications show significant performance gains against state-of-the-art Bayesian Networks and deep-learning baselines. Applications to real-world systems also showcase BSG as an exploratory analysis tool for uncovering indirect spillovers and quantifying systemic risk.