Covariate Regularized Community Detection in Sparse Graphs

Bowei Yan and Purnamrita Sarkar

Journal of the American Statistical Association, 2021, vol. 116, issue 534, 734-745

Abstract: In this article, we investigate community detection in networks in the presence of node covariates. In many instances, covariates and networks individually only give a partial view of the cluster structure. One needs to jointly infer the full cluster structure by considering both. In statistics, an emerging body of work has been focused on combining information from both the edges in the network and the node covariates to infer community memberships. However, so far the theoretical guarantees have been established in the dense regime, where the network can lead to perfect clustering under a broad parameter regime, and hence the role of covariates is often not clear. In this article, we examine sparse networks in conjunction with finite dimensional sub-Gaussian mixtures as covariates under moderate separation conditions. In this setting each individual source can only cluster a nonvanishing fraction of nodes correctly. We propose a simple optimization framework which improves clustering accuracy when the two sources carry partial information about the cluster memberships, and hence perform poorly on their own. Our optimization problem can be solved by scalable convex optimization algorithms. With a variety of simulated and real data examples, we show that the proposed method outperforms other existing methodology. Supplementary materials for this article are available online.

Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/01621459.2019.1706541 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlasa:v:116:y:2021:i:534:p:734-745

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UASA20

DOI: 10.1080/01621459.2019.1706541

Access Statistics for this article

Journal of the American Statistical Association is currently edited by Xuming He, Jun Liu, Joseph Ibrahim and Alyson Wilson

More articles in Journal of the American Statistical Association from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().