The architecture of complex weighted networks
The architecture of complex weighted networks
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DOI：
10.1073/pnas.0400087101
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发表时间：
2004-03-16
影响因子：
11.1
通讯作者：
Vespignani, A
中科院分区：
文献类型：
--
作者：
Barrat, A;Barthélemy, M;Vespignani, A
网络结构出现在各种不同的环境中，例如技术和交通基础设施、社会现象和生物系统。这些高度互连的系统最近成为人们广泛关注的焦点，人们发现并描述了它们的拓扑复杂性。除了复杂的拓扑结构之外，真实网络在连接容量和强度方面也表现出很大的异质性。然而，这些特征在过去的研究中主要没有被考虑，其中链接通常表示为二元状态，即存在或不存在。在这里，我们研究科学协作网络和全球航空运输网络，它们分别是社会和大型基础设施系统的代表性例子。在这两种情况下，都可以为图的每个边分配与网络各个元素之间的连接强度或容量成比例的权重。我们结合加权和拓扑可观测值定义适当的度量，使我们能够表征边和顶点实际强度的复杂统计特性和异质性。这些信息使我们能够研究加权量与网络底层拓扑结构之间的相关性。这些结果在加权网络架构的基础上更好地描述了层次结构和组织原则。
Networked structures arise in a wide array of different contexts such as technological and transportation infrastructures, social phenomena, and biological systems. These highly interconnected systems have recently been the focus of a great deal of attention that has uncovered and characterized their topological complexity. Along with a complex topological structure, real networks display a large heterogeneity in the capacity and intensity of the connections. These features, however, have mainly not been considered in past studies where links are usually represented as binary states, i.e., either present or absent. Here, we study the scientific collaboration network and the world-wide air-transportation network, which are representative examples of social and large infrastructure systems, respectively. In both cases it is possible to assign to each edge of the graph a weight proportional to the intensity or capacity of the connections among the various elements of the network. We define appropriate metrics combining weighted and topological observables that enable us to characterize the complex statistical properties and heterogeneity of the actual strength of edges and vertices. This information allows us to investigate the correlations among weighted quantities and the underlying topological structure of the network. These results provide a better description of the hierarchies and organizational principles at the basis of the architecture of weighted networks.