Bayesian Spillover Graphs for Dynamic Networks

TL;DR

Bayesian Spillover Graphs (BSG) is a new Bayesian method for analyzing dynamic networks, capturing temporal relationships, critical nodes, and uncertainty, with applications in systemic risk and spillover effects.

Contribution

Introduces Bayesian Spillover Graphs, a novel approach combining interpretability and uncertainty quantification for dynamic network analysis.

Findings

BSG outperforms Bayesian Networks and deep learning in identifying source and sink nodes.

Demonstrates effectiveness in real-world systemic risk analysis.

Provides a versatile tool for exploratory analysis of spillovers.

Abstract

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.