Community Recovery on Noisy Stochastic Block Models

Washieu Anan; Gwyneth Liu

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arXiv · 2505.08251

Community Recovery on Noisy Stochastic Block Models

Washieu Anan·Gwyneth Liu

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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.

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Washieu Anan, Gwyneth Liu. 2025-05-13. Community Recovery on Noisy Stochastic Block Models. https://arxiv.org/abs/2505.08251

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