Data clustering using stochastic block models

TL;DR

This paper explores the application of generalized stochastic block models to data clustering, demonstrating their potential advantages over traditional methods like k-means and highlighting their performance on weighted graphs.

Contribution

It introduces a generalized stochastic block model for clustering weighted graphs and compares its effectiveness to existing clustering techniques.

Findings

SBM-based methods outperform k-means in community detection tasks

Generalized SBM can handle weighted graphs effectively

SBM approaches do not require pre-specifying the number of clusters

Abstract

It has been shown that community detection algorithms work better for clustering tasks than other, more popular methods, such as k-means. In fact, network analysis based methods often outperform more widely used methods and do not suffer from some of the drawbacks we notice elsewhere e.g. the number of clusters k usually has to be known in advance. However, stochastic block models which are known to perform well for community detection, have not yet been tested for this task. We discuss why these models cannot be directly applied to this problem and test the performance of a generalization of stochastic block models which work on weighted graphs and compare them to other clustering techniques.