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摘要翻译：
本文首先回顾了复杂网络拓扑结构的几种层次度量方法，然后应用特征选择的概念和方法，对四种典型的理论网络模型，即ERD{o}S-R\'Enyi,Barab\'asi-Albert,Watts-Strogatz以及一种地理类型的网络之间的区分度，量化每种度量方法的相对重要性。所得结果证实，利用测量组合可以很好地分离四个模型。另外，将每个被考虑的特征在二维标准投影中的权重量化为模型整体判别的相对贡献，其中传统聚类系数、层次聚类系数和邻域聚类系数得到了特别有效的结果。有趣的是，平均最短路径长度和层次节点度对四种网络模型的分离贡献很小。
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英文标题：
《What are the Best Hierarchical Descriptors for Complex Networks?》
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作者：
Luciano da F. Costa and Roberto F. S. Andrade
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最新提交年份：
2007
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分类信息：
一级分类：Physics 物理学
二级分类：Disordered Systems and Neural Networks 无序系统与神经网络
分类描述：Glasses and spin glasses; properties of random, aperiodic and quasiperiodic systems; transport in disordered media; localization; phenomena mediated by defects and disorder; neural networks
眼镜和旋转眼镜；随机、非周期和准周期系统的性质；无序介质中的传输；本地化；由缺陷和无序介导的现象；神经网络
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一级分类：Physics 物理学
二级分类：Statistical Mechanics 统计力学
分类描述：Phase transitions, thermodynamics, field theory, non-equilibrium phenomena, renormalization group and scaling, integrable models, turbulence
相变，热力学，场论，非平衡现象，重整化群和标度，可积模型，湍流
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英文摘要：
This work reviews several hierarchical measurements of the topology of complex networks and then applies feature selection concepts and methods in order to quantify the relative importance of each measurement with respect to the discrimination between four representative theoretical network models, namely Erd\"{o}s-R\'enyi, Barab\'asi-Albert, Watts-Strogatz as well as a geographical type of network. The obtained results confirmed that the four models can be well-separated by using a combination of measurements. In addition, the relative contribution of each considered feature for the overall discrimination of the models was quantified in terms of the respective weights in the canonical projection into two dimensions, with the traditional clustering coefficient, hierarchical clustering coefficient and neighborhood clustering coefficient resulting particularly effective. Interestingly, the average shortest path length and hierarchical node degrees contributed little for the separation of the four network models.
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PDF链接：
https://arxiv.org/pdf/705.4251
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