Covariate Regularized Community Detection in Sparse Graphs

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.

keywords:

• Asymptotic analysis
• Kernel method
• Semidefinite programming
• Stochastic block models
• Sub-Gaussian mixture

Supplementary Materials

Title: Additional proofs and experimental results (.pdf file).

Acknowledgements

We thank Arash Amini and Yuan Zhang for generously sharing their code. We are grateful to Soumendu S. Mukherjee, Peter J. Bickel, David Choi, Harrison Zhou, and Qixing Huang for interesting discussions on our article.

Additional information

Funding

PS was partially supported by NSF grant DMS 1713082.