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Divisibility patterns of natural numbers on a complex network

机译:复杂网络上自然数的除数模式

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Investigation of divisibility properties of natural numbers is one of the most important themes in the theory of numbers. Various tools have been developed over the centuries to discover and study the various patterns in the sequence of natural numbers in the context of divisibility. In the present paper, we study the divisibility of natural numbers using the framework of a growing complex network. In particular, using tools from the field of statistical inference, we show that the network is scale-free but has a non-stationary degree distribution. Along with this, we report a new kind of similarity pattern for the local clustering, which we call “stretching similarity”, in this network. We also show that the various characteristics like average degree, global clustering coefficient and assortativity coefficient of the network vary smoothly with the size of the network. Using analytical arguments we estimate the asymptotic behavior of global clustering and average degree which is validated using numerical analysis.
机译:研究自然数的可除性是数字理论中最重要的主题之一。几个世纪以来,人们开发了各种工具来发现和研究在可除性范围内自然数序列中的各种模式。在本文中,我们使用不断发展的复杂网络框架研究自然数的可除性。特别是,使用统计推断领域的工具,我们证明了该网络是无标度的,但是具有非平稳度分布。同时,我们在该网络中报告了一种新的本地集群相似度模式,我们称其为“拉伸相似度”。我们还表明,网络的平均度,全局聚类系数和分类系数等各种特征随网络规模的变化而平滑变化。使用分析参数,我们估计了全局聚类和平均程度的渐近行为,这通过数值分析得到了验证。

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