Network Topology Change-Point Detection from Graph Signals with Prior Spectral Signatures
Network Topology Change-Point Detection from Graph Signals with Prior Spectral Signatures
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DOI：
10.1109/icassp39728.2021.9413857
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
Chiraag Kaushik;T. Roddenberry;Santiago Segarra
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文献类型：
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作者：
Chiraag Kaushik;T. Roddenberry;Santiago Segarra
本文研究了从图信号中检测序列图拓扑变点的问题。我们假设图的节点上的信号通过图过滤模型由底层图结构正则化，然后我们利用该模型将图拓扑变点检测问题提取为子空间检测问题。我们演示了如何将先验信息的光谱签名的变化后的图形可以隐式地去噪所观察到的序列数据，从而导致一个自然的基于AUCUM的算法的变点检测。数值实验说明了我们提出的方法的性能，特别是强调（潜在的噪声）先验信息的好处。
We consider the problem of sequential graph topology change-point detection from graph signals. We assume that signals on the nodes of the graph are regularized by the underlying graph structure via a graph filtering model, which we then leverage to distill the graph topology change-point detection problem to a subspace detection problem. We demonstrate how prior information on the spectral signature of the post-change graph can be incorporated to implicitly denoise the observed sequential data, thus leading to a natural CUSUM-based algorithm for change-point detection. Numerical experiments illustrate the performance of our proposed approach, particularly underscoring the benefits of (potentially noisy) prior information.