Community Recovery on Noisy Stochastic Block Models

Fuente: arXiv
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Hauptverfasser: Anan, Washieu, Liu, Gwyneth
Format: Preprint
Veröffentlicht: 2025
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author Anan, Washieu
Liu, Gwyneth
author_facet Anan, Washieu
Liu, Gwyneth
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2505_08251
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Community Recovery on Noisy Stochastic Block Models
Anan, Washieu
Liu, Gwyneth
Social and Information Networks
Probability
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.
title Community Recovery on Noisy Stochastic Block Models
topic Social and Information Networks
Probability
url https://arxiv.org/abs/2505.08251