Community Recovery on Noisy Stochastic Block Models

TL;DR

This paper introduces a novel attention-based spectral operator and a denoising algorithm that together significantly enhance community detection accuracy in noisy stochastic block models, outperforming existing methods.

Contribution

The work presents MASO, an attention-based spectral operator, and GeoDe, a denoising algorithm, demonstrating their effectiveness in improving community recovery in noisy SBMs.

Findings

GeoDe+MASO outperforms existing methods on noisy SBMs.

Using GeoDe+MASO as a denoising step improves belief propagation recovery by 79.7%.

The combined approach significantly enhances community detection accuracy.

Abstract

We study the problem of community recovery in geometrically-noised stochastic block models (SBM). This work presents two primary contributions: (1) Motif--Attention Spectral Operator (MASO), an attention-based spectral operator that improves upon traditional spectral methods; and (2) Iterative Geometric Denoising (GeoDe), a configurable denoising algorithm that boosts spectral clustering performance. We demonstrate that the fusion of GeoDe+MASO significantly outperforms existing community detection methods on noisy SBMs. Furthermore, we show that using GeoDe+MASO as a denoising step improves belief propagation's community recovery by 79.7% on the Amazon Metadata dataset.