Locality Statistics for Anomaly Detection in Time Series of Graphs
Locality Statistics for Anomaly Detection in Time Series of Graphs
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DOI：
10.1109/tsp.2013.2294594
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发表时间：
2013-06
影响因子：
5.4
通讯作者：
Heng Wang;M. Tang;Youngser Park;C. Priebe
中科院分区：
文献类型：
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作者：
Heng Wang;M. Tang;Youngser Park;C. Priebe
在图形信号处理新兴学科的许多应用中，检测动态网络或图形时间序列中的变化点的能力是一项日益重要的任务。本文将变化点检测制定为生成潜在位置模型的假设检验问题，重点关注随机块模型时间序列的特殊情况。我们根据文献中提出的不同的底层局部性统计数据来分析两类扫描统计数据。我们的主要贡献是推导竞争扫描统计的限制属性和功率特性。从理论上，根据合成数据，并根据安然电子邮件语料库，对性能进行比较。
The ability to detect change-points in a dynamic network or a time series of graphs is an increasingly important task in many applications of the emerging discipline of graph signal processing. This paper formulates change-point detection as a hypothesis testing problem in terms of a generative latent position model, focusing on the special case of the Stochastic Block Model time series. We analyze two classes of scan statistics, based on distinct underlying locality statistics presented in the literature. Our main contribution is the derivation of the limiting properties and power characteristics of the competing scan statistics. Performance is compared theoretically, on synthetic data, and empirically, on the Enron email corpus.