基于邻域度分布的基础设施网络分类
Classification of infrastructure networks by neighborhood degree distribution
摘要：
对网络连通性进行分类的一种常见方法是将节点度分布与特定的概率分布模型相关联。在过去的几十年里，研究人员使用泊松分布或帕累托分布对许多网络进行了分类。城市基础设施，如交通（铁路、公路等）（天然气、水、能源等）系统是一种特殊的网络，受到其构建环境的空间特征的强烈约束。因此，这种网络的节点度跨越非常小的范围，不允许使用节点度分布进行可靠的分类。为了克服这个问题，我们在这里（i）定义邻居度，等于最近的拓扑邻居、相邻节点的节点度之和;（ii）建议使用邻居度来对基础设施网络进行分类。这样的邻域度跨越比标准度更宽的度范围，允许从统计学的角度以更可靠的方式推断概率模型。为了测试我们的建议，我们在这里分析了22个真实的供水网络，建立在不同的环境中，表明泊松分布一般模型非常好的邻域度分布。这一结果似乎与使用标准节点度分布的稀缺信息实现的不太可靠的分类一致。
英文摘要：
A common way of classifying network connectivity is the association of the nodal degree distribution to specific probability distribution models. During the last decades, researchers classified many networks using the Poisson or Pareto distributions. Urban infrastructures, like transportation (railways, roads, etc.) and distribution (gas, water, energy, etc.) systems, are peculiar networks strongly constrained by spatial characteristics of the environment where they are constructed. Consequently, the nodal degree of such networks spans very small ranges not allowing a reliable classification using the nodal degree distribution. In order to overcome this problem, we here (i) define the neighborhood degree, equal to the sum of the nodal degrees of the nearest topological neighbors, the adjacent nodes and (ii) propose to use neighborhood degree to classify infrastructure networks. Such neighborhood degree spans a wider range of degrees than the standard one allowing inferring the probabilistic model in a more reliable way, from a statistical standpoint. In order to test our proposal, we here analyze twenty-two real water distribution networks, built in different environments, demonstrating that the Poisson distribution generally models very well their neighborhood degree distributions. This result seems consistent with the less reliable classification achievable with the scarce information using the standard nodal degree distribution.
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