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Geometric inhomogeneous random graphs

机译:几何不均匀随机图

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Real-world networks, like social networks or the internet infrastructure, have structural properties such as large clustering coefficients that can best be described in terms of an underlying geometry. This is why the focus of the literature on theoretical models for real-world networks shifted from classic models without geometry, such as Chung-Lu random graphs, to modern geometry-based models, such as hyperbolic random graphs.
机译:现实世界网络,如社交网络或互联网基础设施,具有结构特性,例如可以最好地描述底层几何形状的大型聚类系数。 这就是为什么文献的重点是真实网络的理论模型从经典模型转移,没有几何形状,例如chung-lu随机图,到现代的基于几何的模型,例如双曲线随机图。

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