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Main Authors: Liu, Qingyang, Fan, Tianlong, Pan, Liming, Lü, Linyuan
Format: Preprint
Published: 2025
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Online Access:https://arxiv.org/abs/2509.20788
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author Liu, Qingyang
Fan, Tianlong
Pan, Liming
Lü, Linyuan
author_facet Liu, Qingyang
Fan, Tianlong
Pan, Liming
Lü, Linyuan
contents Identifying an optimal set of driver nodes to achieve synchronization via pinning control is a fundamental challenge in complex network science, limited by computational intractability and the lack of general theory. Here, leveraging a degree-based mean-field (annealed) approximation from statistical physics, we analytically reveal how the structural degree distribution systematically governs synchronization performance, and derive an analytic characterization of the globally optimal pinning set and constructive algorithms with linear complexity (dominated by degree sorting, O(N+M). The optimal configuration exhibits a chaotic dependence--a discontinuous sensitivity--on its cardinality, whereby adding a single node can trigger abrupt changes in node composition and control effectiveness. This structural transition fundamentally challenges traditional heuristics that assume monotonic performance gains with budget. Systematic experiments on synthetic and empirical networks confirm that the proposed approach consistently outperforms degree-, betweenness-, and other centrality-based baselines. Furthermore, we quantify how key degree-distribution features--low-degree saturation, high-degree cutoff, and the power-law exponent--govern achievable synchronizability and shape the form of optimal sets. These results offer a systematic understanding of how degree heterogeneity shapes the network controllability. Our work establishes a unified link between degree heterogeneity and spectral controllability, offering both mechanistic insights and practical design rules for optimal driver-node selection in diverse complex systems.
format Preprint
id arxiv_https___arxiv_org_abs_2509_20788
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Revealing Chaotic Dependence and Degree-Structure Mechanisms in Optimal Pinning Control of Complex Networks
Liu, Qingyang
Fan, Tianlong
Pan, Liming
Lü, Linyuan
Systems and Control
Identifying an optimal set of driver nodes to achieve synchronization via pinning control is a fundamental challenge in complex network science, limited by computational intractability and the lack of general theory. Here, leveraging a degree-based mean-field (annealed) approximation from statistical physics, we analytically reveal how the structural degree distribution systematically governs synchronization performance, and derive an analytic characterization of the globally optimal pinning set and constructive algorithms with linear complexity (dominated by degree sorting, O(N+M). The optimal configuration exhibits a chaotic dependence--a discontinuous sensitivity--on its cardinality, whereby adding a single node can trigger abrupt changes in node composition and control effectiveness. This structural transition fundamentally challenges traditional heuristics that assume monotonic performance gains with budget. Systematic experiments on synthetic and empirical networks confirm that the proposed approach consistently outperforms degree-, betweenness-, and other centrality-based baselines. Furthermore, we quantify how key degree-distribution features--low-degree saturation, high-degree cutoff, and the power-law exponent--govern achievable synchronizability and shape the form of optimal sets. These results offer a systematic understanding of how degree heterogeneity shapes the network controllability. Our work establishes a unified link between degree heterogeneity and spectral controllability, offering both mechanistic insights and practical design rules for optimal driver-node selection in diverse complex systems.
title Revealing Chaotic Dependence and Degree-Structure Mechanisms in Optimal Pinning Control of Complex Networks
topic Systems and Control
url https://arxiv.org/abs/2509.20788