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[2602.22446] ECHO: Encoding Communities via High-order Operators
Summary
The paper presents ECHO, a scalable architecture for community detection in attributed networks, addressing limitations of topological algorithms and Graph Neural Networks (GNNs).
Why It Matters
ECHO's innovative approach to community detection tackles significant challenges in machine learning, particularly in handling large, complex networks. By improving scalability and accuracy, it has implications for various applications, including social network analysis and data science.
Key Takeaways
- ECHO introduces a novel architecture that enhances community detection in attributed networks.
- It overcomes computational bottlenecks faced by traditional GNNs and topological algorithms.
- The method achieves high accuracy and speed, processing over 2,800 nodes per second.
- ECHO's design allows for adaptability across different hardware constraints.
- Extensive evaluations show its effectiveness on both synthetic and real-world networks.
Computer Science > Machine Learning arXiv:2602.22446 (cs) [Submitted on 25 Feb 2026] Title:ECHO: Encoding Communities via High-order Operators Authors:Emilio Ferrara View a PDF of the paper titled ECHO: Encoding Communities via High-order Operators, by Emilio Ferrara View PDF HTML (experimental) Abstract:Community detection in attributed networks faces a fundamental divide: topological algorithms ignore semantic features, while Graph Neural Networks (GNNs) encounter devastating computational bottlenecks. Specifically, GNNs suffer from a Semantic Wall of feature over smoothing in dense or heterophilic networks, and a Systems Wall driven by the O(N^2) memory constraints of pairwise clustering. To dismantle these barriers, we introduce ECHO (Encoding Communities via High order Operators), a scalable, self supervised architecture that reframes community detection as an adaptive, multi scale diffusion process. ECHO features a Topology Aware Router that automatically analyzes structural heuristics sparsity, density, and assortativity to route graphs through the optimal inductive bias, preventing heterophilic poisoning while ensuring semantic densification. Coupled with a memory sharded full batch contrastive objective and a novel chunked O(N \cdot K) similarity extraction method, ECHO completely bypasses traditional O(N^2) memory bottlenecks without sacrificing the mathematical precision of global gradients. Extensive evaluations demonstrate that this topology feature synergy co...
