The Annals of Statistics

Convexified modularity maximization for degree-corrected stochastic block models

Yudong Chen, Xiaodong Li, and Jiaming Xu

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Abstract

The stochastic block model (SBM), a popular framework for studying community detection in networks, is limited by the assumption that all nodes in the same community are statistically equivalent and have equal expected degrees. The degree-corrected stochastic block model (DCSBM) is a natural extension of SBM that allows for degree heterogeneity within communities. To find the communities under DCSBM, this paper proposes a convexified modularity maximization approach, which is based on a convex programming relaxation of the classical (generalized) modularity maximization formulation, followed by a novel doubly-weighted $\ell_{1}$-norm $k$-medoids procedure. We establish nonasymptotic theoretical guarantees for approximate and perfect clustering, both of which build on a new degree-corrected density gap condition. Our approximate clustering results are insensitive to the minimum degree, and hold even in sparse regime with bounded average degrees. In the special case of SBM, our theoretical guarantees match the best-known results of computationally feasible algorithms. Numerically, we provide an efficient implementation of our algorithm, which is applied to both synthetic and real-world networks. Experiment results show that our method enjoys competitive performance compared to the state of the art in the literature.

Article information

Source
Ann. Statist., Volume 46, Number 4 (2018), 1573-1602.

Dates
Received: January 2016
Revised: April 2017
First available in Project Euclid: 27 June 2018

Permanent link to this document
https://projecteuclid.org/euclid.aos/1530086426

Digital Object Identifier
doi:10.1214/17-AOS1595

Mathematical Reviews number (MathSciNet)
MR3819110

Zentralblatt MATH identifier
06936471

Subjects
Primary: 62H30: Classification and discrimination; cluster analysis [See also 68T10, 91C20] 91C20: Clustering [See also 62H30]

Keywords
Community detection modularity maximization degree-corrected stochastic block model convex relaxation $k$-medians social network

Citation

Chen, Yudong; Li, Xiaodong; Xu, Jiaming. Convexified modularity maximization for degree-corrected stochastic block models. Ann. Statist. 46 (2018), no. 4, 1573--1602. doi:10.1214/17-AOS1595. https://projecteuclid.org/euclid.aos/1530086426


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Supplemental materials

  • Additional experiments and remaining proofs. In this supplement [12], we provide additional numerical results and the remaining proofs of the theoretical results.