论文标题
量化和比较复杂系统中的结构相关性和异质性的首次计时时间
First-passage times to quantify and compare structural correlations and heterogeneity in complex systems
论文作者
论文摘要
实际上，复杂系统的所有新兴属性都源于其元素行为的非均匀性质以及它们之间的相互作用的性质。但是，对于任何系统的方法，可以在不同类型的系统中量化它们的系统方法，这一事实可以同时出现在本地，介绍和全球量表上，这一事实是一个具体的挑战。我们在这里开发了一个可扩展和非参数框架，以根据在其单位之间的基本交互网络上随机步行的统计数据来表征复杂系统中异质性和相关性的存在。特别是，我们专注于有意义的预分配节点类别之间的归一化平均第一段时间，并展示了它们的各种潜在应用。我们发现，拟议的框架能够在投票系统中表征两极分化，包括英国英国脱欧公投和美国国会的滚动电话投票。此外，班级的分布意味着第一通道时间可以帮助识别负责疾病在社会系统中传播的主要参与者，并比较美国城市的空间隔离，从而揭示了城市流动性在塑造社会经济不平等现象的核心作用。
Virtually all the emergent properties of a complex system are rooted in the non-homogeneous nature of the behaviours of its elements and of the interactions among them. However, the fact that heterogeneity and correlations can appear simultaneously at local, mesoscopic, and global scales, is a concrete challenge for any systematic approach to quantify them in systems of different types. We develop here a scalable and non-parametric framework to characterise the presence of heterogeneity and correlations in a complex system, based on the statistics of random walks over the underlying network of interactions among its units. In particular, we focus on normalised mean first passage times between meaningful pre-assigned classes of nodes, and we showcase a variety of their potential applications. We found that the proposed framework is able to characterise polarisation in voting systems, including the UK Brexit referendum and the roll-call votes in the US Congress. Moreover, the distributions of class mean first passage times can help identifying the key players responsible for the spread of a disease in a social system, and comparing the spatial segregation of US cities, revealing the central role of urban mobility in shaping the incidence of socio-economic inequalities.
