比较网络的比较方法。,Scientific Reports

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比较网络的比较方法。
Scientific Reports ( IF 3.8 ) Pub Date : 2019-11-26 , DOI: 10.1038/s41598-019-53708-y
Mattia Tantardini ₁ , Francesca Ieva _{2,

3} , Lucia Tajoli ₄ , Carlo Piccardi ₅

Affiliation

随着可用数据的惊人增长和网络建模的灵活性，出现了设计有效的定量方法进行网络比较的问题。已经设计了许多这样的方法来完成此任务：它们中的大多数仅处理未定向和未加权的网络，但是也有一些能够处理定向和/或加权的网络，从而适当地利用了更丰富的信息。在这项工作中，我们致力于比较各种比较网络的方法，并为选择合适的方法提供指导。首先，我们对网络比较方法的集合进行回顾和分类，重点介绍它们所基于的标准以及它们的优缺点。该集合包括需要已知节点对应关系的方法，例如DeltaCon和Cut Distance，以及不需要先验已知的节点对应的方法（例如基于对齐，基于图小图和光谱的方法），以及最近提出的Portrait Divergence和NetLSD。我们在合成网络上测试了上述方法，并评估了它们的可用性以及它们提供的结果的意义。最后，我们将这些方法应用于两个真实的数据集，即欧洲航空运输网络和粮农组织贸易网络，以讨论可以从此类分析中得出的结果。

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Comparing methods for comparing networks.

With the impressive growth of available data and the flexibility of network modelling, the problem of devising effective quantitative methods for the comparison of networks arises. Plenty of such methods have been designed to accomplish this task: most of them deal with undirected and unweighted networks only, but a few are capable of handling directed and/or weighted networks too, thus properly exploiting richer information. In this work, we contribute to the effort of comparing the different methods for comparing networks and providing a guide for the selection of an appropriate one. First, we review and classify a collection of network comparison methods, highlighting the criteria they are based on and their advantages and drawbacks. The set includes methods requiring known node-correspondence, such as DeltaCon and Cut Distance, as well as methods not requiring a priori known node-correspondence, such as alignment-based, graphlet-based, and spectral methods, and the recently proposed Portrait Divergence and NetLSD. We test the above methods on synthetic networks and we assess their usability and the meaningfulness of the results they provide. Finally, we apply the methods to two real-world datasets, the European Air Transportation Network and the FAO Trade Network, in order to discuss the results that can be drawn from this type of analysis.

更新日期：2019-11-27

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