Alstott Jeff, Panzarasa Pietro, Rubinov Mikail, Bullmore Edward T, Vértes Petra E
1] Department of Psychiatry, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge CB2 0SZ UK [2] Section on Critical Brain Dynamics, National Institute of Mental Health, Bethesda, 20892 Maryland, USA.
School of Business and Management, Queen Mary University of London, London, E1 4NS UK.
Sci Rep. 2014 Dec 1;4:7258. doi: 10.1038/srep07258.
Network analysis can help uncover meaningful regularities in the organization of complex systems. Among these, rich clubs are a functionally important property of a variety of social, technological and biological networks. Rich clubs emerge when nodes that are somehow prominent or 'rich' (e.g., highly connected) interact preferentially with one another. The identification of rich clubs is non-trivial, especially in weighted networks, and to this end multiple distinct metrics have been proposed. Here we describe a unifying framework for detecting rich clubs which intuitively generalizes various metrics into a single integrated method. This generalization rests upon the explicit incorporation of randomized control networks into the measurement process. We apply this framework to real-life examples, and show that, depending on the selection of randomized controls, different kinds of rich-club structures can be detected, such as topological and weighted rich clubs.
网络分析有助于揭示复杂系统组织中有意义的规律。其中，富俱乐部是各种社会、技术和生物网络的一个功能上重要的属性。当以某种方式突出或“富有”（例如，连接高度密集）的节点相互之间优先互动时，富俱乐部就会出现。富俱乐部的识别并非易事，尤其是在加权网络中，为此人们提出了多种不同的度量方法。在这里，我们描述了一个用于检测富俱乐部的统一框架，该框架直观地将各种度量方法概括为一种单一的综合方法。这种概括基于在测量过程中明确纳入随机对照网络。我们将这个框架应用于实际例子，并表明，根据随机对照的选择，可以检测到不同类型的富俱乐部结构，如拓扑富俱乐部和加权富俱乐部。
