arXiv subjects

Kaiming Luo

Publications and source records attributed to Kaiming Luo.

7 recordsLinked to original sources

arXiv2026

Functional Dismantling of Network Relaxation through Slow-Branch Susceptibility

Robustness of relaxation on asymmetric networks is not determined by connectivity alone, because the slow collective mode can be complex and may change its spectral identity under adaptive damage. We introduce a slow-branch susceptibility framework for functional dismantling of network relaxation. Starting from the projected relaxation dynamics, we show that the relevant robustness observable is the real part of the selected nonzero Laplacian branch, which controls the long-time decay of the nonstationary sector. Node deletion is then treated as a dimension-changing compression of the operator, leading to a modal susceptibility (MS) score that estimates the first-order reduction of the branch-tracked relaxation rate from the biorthogonal support of the slow mode. In the reciprocal limit, the same construction reduces to the weighted Fiedler sector, placing directed and weighted-undirected networks within a common spectral-response formulation. Tests on synthetic and real-world networks show that MS identifies vulnerability patterns that differ from standard centrality-based attacks and edge-level spectral proxies. These results resolve a modal-selection ambiguity in non-Hermitian robustness analysis and provide a spectral basis for functional dismantling in asymmetric networks.

physics.soc-ph↗

arXiv2026

Shear, Not Coherence, Organizes chaotic response under Higher-Order Coupling

What dynamical quantity is actually controlled by higher-order interactions in chaotic oscillator networks remains unclear. In amplitude-active systems, chaos is often interpreted through coherence, yet coherence is not the quantity that governs instability. In this work, we study a minimal globally coupled quartet of nonisochronous Stuart-Landau oscillators with pairwise and symmetric three-body interactions. The pairwise baseline already supports a connected chaotic branch, and higher-order coupling reconstructs rather than creates this irregular dynamics. We show that chaos is organized not by phase coherence but by effective-frequency shear: higher-order coupling regulates amplitude heterogeneity, which nonisochronicity converts into shear, and shear controls how chaos is expressed under higher-order coupling. The Lyapunov response collapses onto a reduced shear-based description, revealing an indirect control pathway. These results establish that higher-order interactions control chaos only indirectly, by regulating an amplitude-shear mechanism rather than acting directly on synchrony.

nlin.CD↗

arXiv2026

Hidden Higher-Order Vulnerabilities in Simplicial Complexes Revealed by Branch-Consistent Functional Robustness

Robustness of higher-order networks is often quantified by the instantaneous smallest positive eigenvalue of the Hodge $1$-Laplacian under simplex deletion. We show that this observable is generically ill-defined: along a deletion trajectory, eigenvalue branches can switch, so the quantity being monitored may correspond to different nonharmonic modes at different steps. The primary issue is therefore definitional rather than algorithmic. We resolve it by fixing the first nonharmonic branch of the intact complex and following that same branch throughout the damage process, which defines a branch-consistent functional robustness. Triangle sensitivities then follow directly from first-order perturbation theory, making the resulting mode-sensitive deletion protocol a consequence of the observable itself rather than an independent heuristic. Across synthetic and empirical clique complexes, removing only a small fraction of triangles is sufficient to drive the tracked mode to collapse, while graph-level observables remain unchanged because the $1$-skeleton is exactly preserved. The same framework also reveals bridge-like localization of functionally critical simplices and provides a compact predictor of dynamical timescales.

nlin.AO↗

arXiv2026

Controlling synchronization dynamics via physics-informed neural networks

Synchronization control in networked dynamical systems requires regulating not only whether coherence is achieved, but also when and to what extent it emerges. We propose a physics-informed neural network (PINN) framework for continuous-time synchronization regulation, in which system trajectories and control inputs are jointly parameterized and constrained by the governing dynamics. Macroscopic synchronization objectives are imposed directly at the trajectory level by enforcing persistence conditions on the order parameter after a prescribed target time. This formulation enables simultaneous control of synchronization time and coherence level without assuming any explicit feedback law or solving a strict optimal control problem. Numerical studies on networked Kuramoto oscillators demonstrate smooth synchronization with reduced transient control effort and competitive cumulative cost relative to analytical baselines. The framework remains effective in non-gradient and frustrated dynamics, highlighting physics-informed neural control as a flexible trajectory-level approach to synchronization regulation.

nlin.CD↗

arXiv2025

Variational Physics-Informed Ansatz for Reconstructing Hidden Interaction Networks from Steady States

The interaction structure of a complex dynamical system governs its collective behavior, yet existing reconstruction methods struggle with nonlinear, heterogeneous, and higher-order couplings, especially when only steady states are observable. We propose a Variational Physics-Informed Ansatz (VPIA) that infers general interaction operators directly from heterogeneous steady-state data. VPIA embeds the steady-state constraints of the dynamics into a differentiable variational representation and reconstructs the underlying couplings by minimizing a physics-derived steady-state residual, without requiring temporal trajectories, derivative estimation, or supervision. Residual sampling combined with natural-gradient optimization enables scalable learning of large and higher-order networks. Across diverse nonlinear systems, VPIA accurately recovers directed, weighted, and multi-body structures under substantial noise, providing a unified and robust framework for physics-constrained inference of complex interaction networks in settings where only snapshot observations are available.

cs.LG↗

arXiv2025

Locally Optimal Percolation for Network Resilience Dismantling via Fiedler Vector Gradient Iterative Attack

Network resilience, dynamically quantified by the Fiedler value ($\lambda_2$,the second smallest eigenvalue of the Laplacian matrix) ensures functional stability and efficient energy transmission, yet also introduces vulnerabilities that dismantling the resilience of the network can cause a functional breakdown of the network. However, traditional percolation strategies focused on structural attacks often fail to effectively affect resilience and lack universal applicability. Here, we employ a Laplacian spectral perturbation approach to systematically identify and remove edges critical to resilience. We derive the sensitivity of $\lambda_2$ to topological changes and employ the gradient of Fiedler vector to measure each edge's contribution of resilience, revealing an intrinsic relationship to community partition. Accordingly, we propose the Fiedler Gradient Iterative Attack (FGIA) algorithm, which constructs locally optimal edge removal sequences to maximize $\lambda_2$ degradation with significantly lower computational cost than brute-force methods. Our results offer a rigorous approach for inducing controlled resilience collapse, with potential applications in neuroscience and critical infrastructure protection.

nlin.CD↗

arXiv2025

Hierarchical Synchronization and Distortion Scaling in Social Media Networks: A Fractal-Like Topology Theory

The rapid proliferation of social media as a dominant channel for information dissemination has intensified concerns over systemic information distortion, whereby content is progressively altered through successive layers of transmission. While prior studies have explored such distortion qualitatively, the quantitative interplay between propagation topology and stochastic cognitive perturbations remains insufficiently understood. In this work, we propose a novel fractal-inspired directed hierarchical network model to capture the structural patterns of propagation, and introduce a Noise-Frustrated Hegselmann-Krause (NFHK) framework to model opinion dynamics under noise. Analytical results, supported by group and graph theory, reveal that noise accumulation leads to increasing opinion distortion and the emergence of intra-layer synchronization. Multi-agent simulations confirm these effects, showing that noise intensity shapes both convergence rates and weak intra-layer clustering. Empirical validation using a representative retweet cascade demonstrates that the proposed model reproduces real-world distortion patterns and synchronization behaviors, even without direct links. This work uncovers a unified mechanism for information distortion in digital platforms and offers topology-aware insights for public opinion governance and platform regulation.

physics.soc-ph↗