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Modeling network growth with assortative mixing

机译:使用混合混合对网络增长进行建模

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We propose a model of an underlying mechanism responsible for the formation of assortative mixing in networks between "similar" nodes or vertices based on generic vertex properties. Existing models focus on a particular type of assortative mixing, such as mixing by vertex degree, or present methods of generating a network with certain properties, rather than modeling a mechanism driving assortative mixing during network growth. The motivation is to model assortative mixing by non-topological vertex properties, and the influence of these non-topological properties on network topology. The model is studied in detail for discrete and hierarchical vertex properties, and we use simulations to study the topology of resulting networks. We show that assortative mixing by generic properties directly drives the formation of community structure beyond a threshold assortativity of r similar to 0.5, which in turn influences other topological properties. This direct relationship is demonstrated by introducing a new measure to characterise the correlation between assortative mixing and community structure in a network. Additionally, we introduce a novel type of assortative mixing in systems with hierarchical vertex properties, from which a hierarchical community structure is found to result.
机译:我们提出了一个基础机制模型,该模型负责基于通用顶点属性在“相似”节点或顶点之间的网络中形成分类混合。现有的模型关注于特定类型的分类混合,例如按顶点度混合,或当前的生成具有某些属性的网络的方法,而不是对在网络增长过程中驱动分类混合的机制进行建模。其动机是通过非拓扑顶点属性以及这些非拓扑属性对网络拓扑的影响来建模分类混合。该模型针对离散和分层的顶点属性进行了详细研究,我们使用仿真来研究所得网络的拓扑。我们表明,通过通用属性进行的分类混合直接驱动了社区结构的形成,超出了类似于0.5的r的阈值分类,进而影响了其他拓扑属性。通过引入一种新方法来表征网络中分类混合与社区结构之间的相关性,可以证明这种直接关系。此外,我们在具有分层顶点属性的系统中引入了一种新型的混合混合,从中可以找到分层社区结构。

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