Coupling Asymmetry Optimizes Collective Dynamics Over Multiplex Networks
Coupling Asymmetry Optimizes Collective Dynamics Over Multiplex Networks
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DOI：
10.1109/tnse.2023.3325278
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发表时间：
2021-06
影响因子：
6.6
通讯作者：
Z. Song;D. Taylor
中科院分区：
文献类型：
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作者：
Z. Song;D. Taylor
网络往往是相互关联的，一个系统对另一个系统具有更大的影响力。然而，这种不对称性对自组织现象的影响（例如，一致性和同步）没有得到很好的理解。在这里，我们研究集体动力学使用广义图拉普拉斯多路复用网络包含层是不对称耦合。我们探讨了耦合不对称性对集体状态收敛速度的非线性影响，发现不对称性会导致一个或多个最优解，从而最大限度地加速收敛。当一个更快的系统和一个更慢的系统耦合时，根据它们的相对时间尺度，它们的最佳耦合要么是合作的（网络层相互依赖），要么是非合作的（一个网络引导另一个网络，而没有相互的影响）。快速系统对慢速系统产生更强的影响通常是最佳的，但与直觉相反，相反的情况也可能是正确的。作为一个应用程序，我们为人类人工智能系统的集体决策建模，其中社交网络由人工智能代理网络支持，发现合作最佳要求这两个网络在足够相似的时间尺度上运行。更广泛地说，我们的工作突出了耦合不对称性和时间尺度平衡的优化作为互联系统的集体行为设计的基本概念。
Networks are often interconnected, with one system wielding greater influence over another. However, the effects of such asymmetry on self-organized phenomena (e.g., consensus and synchronization) are not well understood. Here, we study collective dynamics using a generalized graph Laplacian for multiplex networks containing layers that are asymmetrically coupled. We explore the nonlinear effects of coupling asymmetry on the convergence rate toward a collective state, finding that asymmetry induces one or more optima that maximally accelerate convergence. When a faster and a slower system are coupled, depending on their relative timescales, their optimal coupling is either cooperative (network layers mutually depend on one another) or non-cooperative (one network directs another without a reciprocated influence). It is often optimal for the faster system to more-strongly influence the slower one, yet counter-intuitively, the opposite can also be true. As an application, we model collective decision-making for a human-AI system in which a social network is supported by an AI-agent network, finding that a cooperative optimum requires that these two networks operate on a sufficiently similar timescale. More broadly, our work highlights the optimization of coupling asymmetry and timescale balancing as fundamental concepts for the design of collective behavior over interconnected systems.