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Construction of Growing Graphs with Given Power-Law Asymptotics of Vertex Degree Distributions

机译:给定幂律渐近度顶点分布的成长图的构造

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Two classes of graphs are considered in the article: preferential attachment graphs with linear weight function and hybrid model of Jackson-Rogers graphs. The research is carried out to find such graphs whose vertex degree distributions have power-law asymptotics with an exponent that belongs to the interval from two to three. It is established for the first time that each hybrid graph corresponds to a certain graph with a linear weight function that has exactly the same vertex degree distribution as a hybrid graph. As a result of the investigation, all graphs of the classes under consideration are revealed, which implement the desired power-law asymptotics of the studied distributions. A formula is derived that allows us to determine the value of the weight function parameter from the given value of the power asymptotics exponent. The reliability and practical significance of the obtained theoretical results are confirmed by an example of their application for graph calibration according to data on a simulated network of autonomous Internet systems.
机译:本文考虑了两类图:具有线性权重函数的优先依附图和杰克逊-罗杰斯图的混合模型。进行研究以找到这样的图,其顶点度分布具有幂律渐近性,且幂指数渐进性属于从2到3的间隔。首次确定每个混合图对应于具有线性权重函数的某个图,该权重函数具有与混合图完全相同的顶点度分布。调查的结果显示了所考虑类别的所有图形,这些图形实现了所研究分布的理想幂律渐近性。得出一个公式,该公式使我们能够从功率渐近指数的给定值确定权重函数参数的值。通过将其用于根据自治Internet系统的模拟网络上的数据进行图形标定的示例,证实了所获得理论结果的可靠性和实际意义。

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