Community Detection in Partial Correlation Network Models
Community Detection in Partial Correlation Network Models
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DOI：
10.1080/07350015.2020.1798241
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发表时间：
2017-06
影响因子：
3
通讯作者：
C. Brownlees;Guðmundur Guðmundsson;G. Lugosi
中科院分区：
文献类型：
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作者：
C. Brownlees;Guðmundur Guðmundsson;G. Lugosi
摘要针对大样本时间序列，提出了一类具有社团结构的偏相关网络模型。在该模型中，序列被划分为潜在组，使得组内的相关性高于组间的相关性。然后，我们提出了一个算法，允许一个检测的社区使用样本协方差矩阵的特征向量。我们研究的过程的属性，并建立其一致性。该方法用于研究美国的真实的活动聚类。
Abstract We introduce a class of partial correlation network models with a community structure for large panels of time series. In the model, the series are partitioned into latent groups such that correlation is higher within groups than between them. We then propose an algorithm that allows one to detect the communities using the eigenvectors of the sample covariance matrix. We study the properties of the procedure and establish its consistency. The methodology is used to study real activity clustering in the United States.