Selecting the Number of Communities for Weighted Degree-Corrected Stochastic Block Models

TL;DR

This paper introduces a new weighted degree-corrected stochastic block model and a sequential testing method to accurately determine the number of communities in weighted networks without full likelihood modeling.

Contribution

The paper proposes a novel weighted DCSBM and a spectral clustering-based sequential testing framework for community detection, with proven consistency and empirical validation.

Findings

Method accurately estimates the true number of communities.

Consistent in various weighted network scenarios.

Performs well on both simulated and real data.

Abstract

We investigate how to select the number of communities for weighted networks without a full likelihood modeling. First, we propose a novel weighted degree-corrected stochastic block model (DCSBM), where the mean adjacency matrix is modeled in the same way as in the standard DCSBM, while the variance profile matrix is assumed to be related to the mean adjacency matrix through a given variance function. Our method of selecting the number of communities is based on a sequential testing framework. In each step, the weighted DCSBM is fitted via some spectral clustering method. A key component of our method is matrix scaling on the estimated variance profile matrix. The resulting scaling factors can be used to normalize the adjacency matrix, from which the test statistic is then obtained. Under mild conditions on the weighted DCSBM, our proposed procedure is shown to be consistent in estimating the true number of communities. Numerical experiments on both simulated and real-world network data demonstrate the desirable empirical properties of our method.