Community Recovery on Noisy Stochastic Block Models (2505.08251v4)

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.