衡量加权富人俱乐部的统一框架
A Unifying Framework for Measuring Weighted Rich Clubs
摘要：
网络分析可以帮助发现复杂系统组织中有意义的缺陷。其中，富人俱乐部是各种社会、技术和生物网络的重要功能属性。当节点在某种程度上突出或“丰富”（例如，高度连接）优先彼此交互。富俱乐部的识别是不平凡的，特别是在加权网络中，为此，已经提出了多个不同的度量。在这里，我们描述了一个统一的框架，检测丰富的俱乐部，直观地概括成一个单一的综合方法的各种指标。这种推广依赖于明确纳入随机控制网络的测量过程。我们将这个框架应用于现实生活中的例子，并表明，根据随机控制的选择，可以检测到不同种类的丰富的俱乐部结构，如拓扑和加权丰富的俱乐部。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
