论文标题
复杂网络的动力鲁棒性受长距离连接的影响
Dynamical robustness of complex networks subject to long-range connectivity
论文作者
论文摘要
尽管有一些尝试了解复杂网络的动态鲁棒性，但与网络上的其他动态过程相比，这一极为重要的研究主题仍在黎明中。在此，我们在此考虑复杂网络的动力学单位之间的远程相互作用的概念，并首次证明这种特征可以对网络系统的动态鲁棒性产生重要影响，而与基础网络拓扑无关。我们在各种网络体系结构上对这种现象进行了全面分析。这种动态损害能够实质性地影响网络性能，确定提高网络鲁棒性的机制成为一个基本问题。在这项工作中，我们提出了一个基于自我反馈的处方，该处方可以有效地复活由主动和不活跃的动力学单元组成的复杂网络的全局节奏性，从而可以增强网络的鲁棒性。在处理所有D-Path邻接矩阵时，我们已经能够分析整个命题，与数值结果非常一致。对于数值计算，我们检查了无规模的网络，瓦特 - 史特罗格兹小世界模型以及Erdös-rényi随机网络，以及Landau-Stuart振荡器，用于施放局部动力学。
In spite of a few attempts in understanding the dynamical robustness of complex networks, this extremely important subject of research is still in its dawn as compared to the other dynamical processes on networks. We hereby consider the concept of long-range interactions among the dynamical units of complex networks and demonstrate for the first time that such a characteristic can have noteworthy impacts on the dynamical robustness of networked systems, regardless of the underlying network topology. We present a comprehensive analysis of this phenomenon on top of diverse network architectures. Such dynamical damages being able to substantially affect the network performance, determining mechanisms that boost the robustness of networks becomes a fundamental question. In this work, we put forward a prescription based upon self-feedback that can efficiently resurrect global rhythmicity of complex networks composed of active and inactive dynamical units, and thus can enhance the network robustness. We have been able to delineate the whole proposition analytically while dealing with all d-path adjacency matrices, having an excellent agreement with the numerical results. For the numerical computations, we examine scale-free networks, Watts-Strogatz small world model and also Erdös-Rényi random network, along with Landau-Stuart oscillators for casting the local dynamics.
