论文标题
合并过程的最大熵网态
Maximum entropy network states for coalescence processes
论文作者
论文摘要
复杂的网络状态的特征是系统的结构和动力学之间的相互作用。代表此类状态的一种方法是通过网络密度矩阵，其von Neumann熵表征了与给定拓扑和动力学演化相兼容的不同微晶格的数量。在这封信中，我们提出了一个最大的熵原理来表征具有异质性，相关性，连通性模式和非平凡动态系统的网络状态。我们专注于三个不同的聚合过程，在经验互连系统的分析中广泛遇到，并表征它们的熵和在不同时间尺度上不同的动态状态之间的过渡。我们的框架允许人们研究聚集的系统的统计物理，例如在服务于相同地理区域的运输基础设施中，或相关，例如在社会相互作用的生物体中产生的脑间同步，以及群体或同步的活性物质。
Complex network states are characterized by the interplay between system's structure and dynamics. One way to represent such states is by means of network density matrices, whose von Neumann entropy characterizes the number of distinct microstates compatible with given topology and dynamical evolution. In this Letter, we propose a maximum entropy principle to characterize network states for systems with heterogeneous, generally correlated, connectivity patterns and non-trivial dynamics. We focus on three distinct coalescence processes, widely encountered in the analysis of empirical interconnected systems, and characterize their entropy and transitions between distinct dynamical regimes across distinct temporal scales. Our framework allows one to study the statistical physics of systems that aggregate, such as in transportation infrastructures serving the same geographic area, or correlate, such as inter-brain synchrony arising in organisms that socially interact, and active matter that swarm or synchronize.
