论文标题
异质随机步行的通用覆盖时间分布
Universal cover-time distribution of heterogeneous random walks
论文作者
论文摘要
封面时间问题,即访问系统中每个站点的时间,是随机步行的关键问题之一,该问题在自然,社会和工程系统中广泛应用。解决复杂结构随机步行的全面覆盖时间的分布一直是一个长期的挑战,并吸引了持续的努力。然而,已知的结果基本上仅限于同质系统,在该系统中,不同的站点处于相等的基础上,并且具有相同或接近平均的第一学期时间,例如在圆环上随机行走。相比之下,现实的随机步行通常是异质的,平均第一学期时间多样化。普遍分布仍然存在吗?在这里,通过考虑最普遍的情况,我们在封面时间内发现了广义的续订关系,利用了以前没有考虑到的多元化平均第一阶段时间。这使我们能够具体建立异质随机步行的重新覆盖时间的普遍分布,事实证明,这是Gumbel通用类别,对于一个极端价值统计的大型家庭而言,它无处不在。我们的分析基于传输矩阵框架,该框架除了异质性外,它还具有符合偏见的协议,有向链接和自连接循环的稳健性。该发现通过在模型和现实拓扑结构上的多种异质非划分随机步行进行广泛的数值模拟来证实。我们的新技术成分可能会因非相同分布的其他极端价值或真诚性问题而被利用。
The cover-time problem, i.e., time to visit every site in a system, is one of the key issues of random walks with wide applications in natural, social, and engineered systems. Addressing the full distribution of cover times for random walk on complex structures has been a long-standing challenge and has attracted persistent efforts. Yet, the known results are essentially limited to homogeneous systems, where different sites are on an equal footing and have identical or close mean first-passage times, such as random walks on a torus. In contrast, realistic random walks are prevailingly heterogeneous with diversified mean first-passage times. Does a universal distribution still exist? Here, by considering the most general situations, we uncover a generalized rescaling relation for the cover time, exploiting the diversified mean first-passage times that have not been accounted for before. This allows us to concretely establish a universal distribution of the rescaled cover times for heterogeneous random walks, which turns out to be the Gumbel universality class that is ubiquitous for a large family of extreme value statistics. Our analysis is based on the transfer matrix framework, which is generic that besides heterogeneity, it is also robust against biased protocols, directed links, and self-connecting loops. The finding is corroborated with extensive numerical simulations of diverse heterogeneous non-compact random walks on both model and realistic topological structures. Our new technical ingredient may be exploited for other extreme value or ergodicity problems with nonidentical distributions.