[2603.20684] Centrality-Based Pruning for Efficient Echo State Networks

Computer Science > Machine Learning arXiv:2603.20684 (cs) [Submitted on 21 Mar 2026] Title:Centrality-Based Pruning for Efficient Echo State Networks Authors:Sudip Laudari View a PDF of the paper titled Centrality-Based Pruning for Efficient Echo State Networks, by Sudip Laudari View PDF HTML (experimental) Abstract:Echo State Networks (ESNs) are a reservoir computing framework widely used for nonlinear time-series prediction. However, despite their effectiveness, the randomly initialized reservoir often contains redundant nodes, leading to unnecessary computational overhead and reduced efficiency. In this work, we propose a graph centrality-based pruning approach that interprets the reservoir as a weighted directed graph and removes structurally less important nodes using centrality measures. Experiments on Mackey-Glass time-series prediction and electric load forecasting demonstrate that the proposed method can significantly reduce reservoir size while maintaining, and in some cases improving, prediction accuracy, while preserving the essential reservoir dynamics. Comments: Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Optimization and Control (math.OC) Cite as: arXiv:2603.20684 [cs.LG]   (or arXiv:2603.20684v1 [cs.LG] for this version)   https://doi.org/10.48550/arXiv.2603.20684 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Sudip Laudari [view email] [v1] Sat, 21 Mar 2026 06:55:50 UTC (3,548 KB) ...