Community Detection in Partial Correlation Network Models
31 Pages Posted: 7 May 2016 Last revised: 16 Jul 2020
Date Written: June 21, 2017
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 U.S.
Keywords: Partial Correlation Networks, Random Graphs, Community Detection, Spectral Clustering, Graphical Models
JEL Classification: C3, C33, C55
Suggested Citation: Suggested Citation