Nartallo-Kaluarachchi Ramón, Asllani Malbor, Deco Gustavo, Kringelbach Morten L, Goriely Alain, Lambiotte Renaud
Mathematical Institute, <a href="https://ror.org/052gg0110">University of Oxford</a>, Woodstock Road, Oxford OX2 6GG, United Kingdom.
Centre for Eudaimonia and Human Flourishing, <a href="https://ror.org/052gg0110">University of Oxford</a>, 7 Stoke Pl, Oxford OX3 9BX, United Kingdom.
Phys Rev E. 2024 Sep;110(3-1):034313. doi: 10.1103/PhysRevE.110.034313.
The structure of a complex network plays a crucial role in determining its dynamical properties. In this paper , we show that the the degree to which a network is directed and hierarchically organized is closely associated with the degree to which its dynamics break detailed balance and produce entropy. We consider a range of dynamical processes and show how different directed network features affect their entropy production rate. We begin with an analytical treatment of a two-node network followed by numerical simulations of synthetic networks using the preferential attachment and Erdös-Renyi algorithms. Next, we analyze a collection of 97 empirical networks to determine the effect of complex real-world topologies. Finally, we present a simple method for inferring broken detailed balance and directed network structure from multivariate time series and apply our method to identify non-equilibrium dynamics and hierarchical organisation in both human neuroimaging and financial time series. Overall, our results shed light on the consequences of directed network structure on non-equilibrium dynamics and highlight the importance and ubiquity of hierarchical organisation and non-equilibrium dynamics in real-world systems.
复杂网络的结构在决定其动力学特性方面起着至关重要的作用。在本文中,我们表明网络的定向和分层组织程度与它的动力学打破细致平衡并产生熵的程度密切相关。我们考虑了一系列动力学过程,并展示了不同的定向网络特征如何影响它们的熵产生率。我们首先对一个双节点网络进行解析处理,然后使用偏好依附和厄多斯 - 雷尼算法对合成网络进行数值模拟。接下来,我们分析了97个实证网络的集合,以确定复杂的现实世界拓扑结构的影响。最后,我们提出了一种从多元时间序列推断打破的细致平衡和定向网络结构的简单方法,并将我们的方法应用于识别人类神经成像和金融时间序列中的非平衡动力学和分层组织。总体而言,我们的结果揭示了定向网络结构对非平衡动力学的影响,并突出了分层组织和非平衡动力学在现实世界系统中的重要性和普遍性。