Community Detection in Partial Correlation Network Models
提出一类具有社区结构的部分相关网络模型，用于大规模时间序列数据，并开发基于样本协方差矩阵特征向量的社区检测算法，证明了方法的一致性，应用于美国实际经济活动聚类。
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.