论文标题
动态网络的贝叶斯溢出图
Bayesian Spillover Graphs for Dynamic Networks
论文作者
论文摘要
我们提出了贝叶斯溢出图（BSG），这是一种学习时间关系，识别关键节点的新方法，并量化了动态系统中多类溢出效应的不确定性。 BSG通过预测误差方差分解（FEVD）和通过贝叶斯时间序列模型的全面不确定性定量来利用可解释的框架，以在系统性风险和预测变异性方面将时间关系与时间关系化。预测地平线超参数$ h $允许学习短期和均衡状态网络行为。在各种图和错误规格下识别源和下沉节点的实验显示，针对最先进的贝叶斯网络和深度学习基线的实验表现出了显着的性能增长。对现实世界系统的应用还展示了BSG作为探索性分析工具，用于发现间接溢出和量化系统性风险。
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.
