Abstract

Community detection in attributed networks faces a structural tension: classical graph algorithms scale efficiently but ignore semantic attributes, while Graph Neural Networks (GNNs) integrate semantics yet collapse under noise and quadratic memory bottlenecks. We introduce ECHO (Encoding Communities via High-Order Operators), a scalable, self-supervised framework that treats communities as regions of adaptive diffusion on semantic manifolds. ECHO couples a topology-aware encoder router, an attention-modulated diffusion operator, a memory-sharded contrastive objective, and a chunked extraction step. Across six standard attributed benchmarks, ECHO attains the best normalized mutual information on every assortative graph, improving over the strongest of nine recent baselines (including heterophily-specific GNNs and a graph transformer) by 5–29 NMI points on Amazon Photo …

Date

2026

Authors

Emilio Ferrara

Journal

ACM AI Letters

Publisher

ACM