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Node Assortativity in Complex Networks: An Alternative Approach

机译:复杂网络中的节点分类性:一种替代方法

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Assortativity quantifies the tendency of nodes being connected to similar nodes in a complex network. Degree Assortativity can be quantified as a Pearson correlation. However, it is insufficient to explain assortative or disassortative tendencies of individual nodes or links, which may be contrary to the overall tendency of the network. A number of ‘local’ assortativity measures have been proposed to address this. In this paper we define and analyse an alternative formulation for node assortativity, primarily for undirected networks. The alternative approach is justified by some inherent shortcomings of existing local measures of assortativity. Using this approach, we show that most real world scale-free networks have disassortative hubs, though we can synthesise model networks which have assortative hubs. Highlighting the relationship between assortativity of the hubs and network robustness, we show that real world networks do display assortative hubs in some instances, particularly when high robustness to targeted attacks is a necessity.
机译:分类性量化了复杂网络中节点连接到相似节点的趋势。程度分类性可以量化为Pearson相关性。但是,不足以解释单个节点或链接的分类或分解趋势,这可能与网络的总体趋势相反。为了解决这个问题,已经提出了许多“本地”分类措施。在本文中,我们定义和分析了主要针对无向网络的节点分类性的替代表达。现有的本地分类方法存在一些固有的缺陷,这证明了替代方法的合理性。使用这种方法,我们可以证明大多数真实世界的无标度网络都具有可分解的集线器,尽管我们可以合成具有可分解的集线器的模型网络。突出显示集线器的可分类性与网络鲁棒性之间的关系,我们显示,在某些情况下,特别是在需要针对目标攻击的高度鲁棒性时,真实世界的网络确实显示了分类集线器。

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