Nodes in networks that exhibit high connectivity, also called “hubs,” play a critical role in determining the structural and functional properties of networked systems. However, there is no clear definition of what constitutes a hub node in a network, and the classification of network hubs in existing work has either been purely qualitative or relies on ad hoc criteria for thresholding continuous data that do not generalize well to networks with certain degree sequences. Here we develop a set of efficient nonparametric methods that classify hub nodes in directed networks using the Minimum Description Length principle, effectively providing a clear and principled definition for network hubs. We adapt our methods to both unweighted and weighted networks, and we demonstrate them in a range of example applications using real and synthetic network data.

中文翻译：

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识别有向网络中的集线器

网络中表现出高连接性的节点（也称为“集线器”）在确定网络系统的结构和功能特性方面发挥着关键作用。然而，对于网络中的中心节点的构成还没有明确的定义，并且现有工作中网络中心的分类要么纯粹是定性的，要么依赖于对连续数据进行阈值化的临时标准，这些标准不能很好地推广到具有某些特征的网络。度序列。在这里，我们开发了一组有效的非参数方法，使用最小描述长度原则对有向网络中的中心节点进行分类，有效地为网络中心提供清晰且有原则的定义。我们使我们的方法适应未加权和加权网络，并使用真实和合成的网络数据在一系列示例应用程序中演示它们。