Pairwise covariates-adjusted block model for community detection

Citation

Huang, Sihan & Feng, Yang (2018). Pairwise covariates-adjusted block model for community detection. arXiv.org. vol. 1807.03469

Abstract

One of the most fundamental problems in network study is community detection. The stochastic block model (SBM) is one widely used model for network data with different estimation methods developed with their community detection consistency results unveiled. However, the SBM is restricted by the strong assumption that all nodes in the same community are stochastically equivalent, which may not be suitable for practical applications. We introduce a pairwise covariates-adjusted stochastic block model (PCABM), a generalization of SBM that incorporates pairwise covariate information. We study the maximum likelihood estimates of the coefficients for the covariates as well as the community assignments. It is shown that both the coefficient estimates of the covariates and the community assignments are consistent under suitable sparsity conditions. Spectral clustering with adjustment (SCWA) is introduced to efficiently solve PCABM. Under certain conditions, we derive the error bound of community estimation under SCWA and show that it is community detection consistent. PCABM compares favorably with the SBM or degree-corrected stochastic block model (DCBM) under a wide range of simulated and real networks when covariate information is accessible.

URL

https://doi.org/10.48550/arXiv.1807.03469

Reference Type

Journal Article

Journal Title

arXiv.org

Author(s)

Huang, Sihan
Feng, Yang

Year Published

2018

Volume Number

1807.03469

DOI

10.48550/arXiv.1807.03469

Reference ID

9331