Abstract

In this paper we create a novel monitoring system to detect changes within a sequence of networks. Specifically, we consider sparse, weighted, directed, and attributed networks. Our approach uses the Hurdle model to capture sparsity and explain the weights of the edges as a function of the node and edge attributes. Here, the weight of an edge represents the number of interactions between two nodes. We then integrate the Hurdle model with a state space model to capture temporal dynamics of the edge formation process. Estimation is performed using an extended Kalman Filter. Statistical process control charts are used to monitor the network sequence in real time in order to identify changes in connectivity patterns that are caused by regime shifts. We show that the proposed methodology outperforms alternative approaches on both synthetic and real data. We also perform a detailed case study on the 2007-2009 financial crisis. Demonstrating the promise of the proposed approach as an early warning system, we show that our method applied to financial interbank lending networks would have raised alarms to the public prior to key events and announcements by the European Central Bank.

摘要

在本文中，我们创建了一个新颖的监视系统来检测一系列网络中的变化。具体来说，我们考虑稀疏网络，加权网络，定向网络和归因网络。我们的方法使用Hurdle模型捕获稀疏性并根据节点和边缘属性来解释边缘的权重。在此，边缘的权重表示两个节点之间的交互次数。然后，我们将Hurdle模型与状态空间模型集成在一起，以捕获边缘形成过程的时间动态。使用扩展的卡尔曼滤波器执行估计。统计过程控制图用于实时监视网络序列，以识别由状态转换引起的连接模式变化。我们表明，所提出的方法在综合数据和真实数据方面均优于替代方法。我们还将对2007-2009年金融危机进行详细的案例研究。证明了提议的方法作为预警系统的希望，我们表明，在欧洲中央银行发生重要事件和宣布之前，将这种方法应用于金融同业拆借网络会向公众发出警报。