Community Detection in Partial Correlation Network Models

31 Pages Posted: 7 May 2016 Last revised: 16 Jul 2020

See all articles by Christian T. Brownlees

Christian T. Brownlees

Universitat Pompeu Fabra - Faculty of Economic and Business Sciences; Barcelona School of Economics

Gudmundur Gudmundsson

Aarhus BSS; Aarhus University - CREATES

Gabor Lugosi

Universitat Pompeu Fabra - Department of Economics and Business (DEB)

Date Written: June 21, 2017

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 U.S.

Keywords: Partial Correlation Networks, Random Graphs, Community Detection, Spectral Clustering, Graphical Models

JEL Classification: C3, C33, C55

Suggested Citation

Brownlees, Christian T. and Gudmundsson, Gudmundur and Lugosi, Gabor, Community Detection in Partial Correlation Network Models (June 21, 2017). Available at SSRN: https://ssrn.com/abstract=2776505 or http://dx.doi.org/10.2139/ssrn.2776505

Christian T. Brownlees (Contact Author)

Universitat Pompeu Fabra - Faculty of Economic and Business Sciences ( email )

Ramon Trias Fargas 25-27
Barcelona, 08005
Spain

HOME PAGE: http://econ.upf.edu/~cbrownlees/

Barcelona School of Economics ( email )

Carrer de Ramon Trias Fargas, 25-27
Barcelona, 08005
Spain

Gudmundur Gudmundsson

Aarhus BSS ( email )

Fuglesangs Allé 4
Aarhus V, 8210
Denmark

Aarhus University - CREATES ( email )

School of Economics and Management
Building 1322, Bartholins Alle 10
DK-8000 Aarhus C
Denmark

Gabor Lugosi

Universitat Pompeu Fabra - Department of Economics and Business (DEB) ( email )

Barcelona, 08005
Spain

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