On the relationship between Gaussian stochastic blockmodels and label propagation algorithms

TL;DR

This paper introduces a Gaussian stochastic blockmodel that aligns with label propagation algorithms for community detection, revealing that node preferences relate to intra-community eigenvector centrality, and demonstrates strong performance on benchmark networks.

Contribution

The paper establishes a theoretical connection between Gaussian stochastic blockmodels and label propagation algorithms, providing new insights into community detection methods.

Findings

Model aligns with label propagation objectives

Node preference relates to eigenvector centrality

Performs well on benchmark networks

Abstract

The problem of community detection receives great attention in recent years. Many methods have been proposed to discover communities in networks. In this paper, we propose a Gaussian stochastic blockmodel that uses Gaussian distributions to fit weight of edges in networks for non-overlapping community detection. The maximum likelihood estimation of this model has the same objective function as general label propagation with node preference. The node preference of a specific vertex turns out to be a value proportional to the intra-community eigenvector centrality (the corresponding entry in principal eigenvector of the adjacency matrix of the subgraph inside that vertex's community) under maximum likelihood estimation. Additionally, the maximum likelihood estimation of a constrained version of our model is highly related to another extension of label propagation algorithm, namely, the label propagation algorithm under constraint. Experiments show that the proposed Gaussian stochastic blockmodel performs well on various benchmark networks.