标题： 随机图沃罗诺伊镶嵌揭示社区结构 显示英文标题

标题： Stochastic graph Voronoi tessellation reveals community structure

摘要： 给定一个网络，其随机选择单元中心的图-Voronoi图的统计系综表现出可转化为对网络大尺度结构信息的性质。 我们定义了一个节点对级别的度量称为{\it Voronoi凝聚力}，它描述了当在网络中随机选择$g$个中心时，共享同一Voronoi单元的概率。 该度量基于全局上下文（整个网络）提供信息，这是一种其他相似性度量所不携带的信息。 我们探讨了这一现象的数学背景及其几种潜在应用。 特别关注了利用该现象进行社区检测的可能性和局限性。 显示英文摘要

摘要： Given a network, the statistical ensemble of its graph-Voronoi diagrams with randomly chosen cell centers exhibits properties convertible into information on the network's large scale structures. We define a node-pair level measure called {\it Voronoi cohesion} which describes the probability for sharing the same Voronoi cell, when randomly choosing $g$ centers in the network. This measure provides information based on the global context (the network in its entirety) a type of information that is not carried by other similarity measures. We explore the mathematical background of this phenomenon and several of its potential applications. A special focus is laid on the possibilities and limitations pertaining to the exploitation of the phenomenon for community detection purposes.