超越平均：从复杂网络中的局部特征检测全局奇异节点
Beyond the average: Detecting global singular nodes from local features in complex networks
摘要：
偏离平均值可以提供有关自然系统组织的有价值的见解。本文将这一重要原则扩展到复杂网络中奇异模体的系统识别和分析。六个测量量化不同的和互补的功能，网络的每个节点周围的连接计算，和多元统计方法应用于识别奇异节点。所提出的概念和方法的潜力说明了不同类型的复杂的现实世界的网络，即美国的航空运输网络，蛋白质-蛋白质相互作用的酵母酿酒酵母和Roget词库网络。得到的奇异模体在网络中具有独特的功能作用。三个经典的理论网络模型也进行了研究，巴拉巴西-阿尔伯特模型导致了对应于枢纽的奇异图案，证实了该方法的潜力。有趣的是，不同类型的奇异节点基序的数量以及它们的实例数量在真实世界的网络中比在任何基准网络中都要高得多。
英文摘要：
Deviations from the average can provide valuable insights about the organization of natural systems. The present article extends this important principle to the systematic identification and analysis of singular motifs in complex networks. Six measurements quantifying different and complementary features of the connectivity around each node of a network were calculated, and multivariate statistical methods applied to identify singular nodes. The potential of the presented concepts and methodology was illustrated with respect to different types of complex real-world networks, namely the US air transportation network, the protein-protein interactions of the yeast Saccharomyces cerevisiae and the Roget thesaurus networks. The obtained singular motifs possessed unique functional roles in the networks. Three classic theoretical network models were also investigated, with the Barabási-Albert model resulting in singular motifs corresponding to hubs, confirming the potential of the approach. Interestingly, the number of different types of singular node motifs as well as the number of their instances were found to be considerably higher in the real-world networks than in any of the benchmark networks.
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