Monitoring sparse and attributed networks with online Hurdle models
Monitoring sparse and attributed networks with online Hurdle models
复制标题
使用在线 Hurdle 模型监控稀疏网络和归因网络
DOI：
10.1080/24725854.2020.1861390
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发表时间：
2021
影响因子：
2.6
通讯作者：
Mankad, Shawn
中科院分区：
文献类型：
--
作者：
Ebrahimi, Samaneh;Reisi-Gahrooei, Mostafa;Paynabar, Kamran;Mankad, Shawn
在本文中，我们创建了一个新颖的监控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、有向网络和归因网络。我们的方法使用 Hurdle 模型来捕获稀疏性并将边的权重解释为节点和边属性的函数。这里，边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相结合，以捕获边缘形成过程的时间动态。使用扩展卡尔曼滤波器进行估计。统计过程控制图用于实时监控网络序列，以识别由状态转变引起的连接模式的变化。我们表明，所提出的方法在合成数据和真实数据上都优于其他方法。我们还对 2007-2009 年金融危机进行了详细的案例研究。我们证明了所提出的方法作为预警系统的前景，我们证明，我们的方法应用于金融银行间借贷网络，可以在欧洲央行的重大事件和公告之前向公众发出警报。
In this article 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.
社交网络中的连接子图
DOI：
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发表时间：
2004
期刊：
影响因子：
--
作者：
C. Faloutsos;K. McCurley;A. Tomkins
通讯作者：
A. Tomkins
系统性风险*
DOI：
--
发表时间：
2012
期刊：
影响因子：
--
作者：
Ohad Kadan;Fangda Liu;Suying Liu
通讯作者：
Suying Liu