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arXiv math/0504589math.PRmath.CO
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非齐次随机图的相变

The phase transition in inhomogeneous random graphs

Bela Bollobas, Svante Janson, Oliver Riordan

摘要:

我们引入了一个非常一般的模型,一个非齐次随机图的边缘之间的独立性,其规模,使边缘的数量是线性的顶点数。这个标度对应于用于研究相变的G(n,p)的p=c/n标度;而且,它似乎是许多大型现实世界图的一个属性。我们的模型包括许多以前研究过的模型作为特例。我们证明,在一个非常弱的假设(预期的边数是“它应该是”)下,模型的许多性质都可以确定,特别是相变的临界点,以及相变上方巨分量的大小。我们通过将随机图与分支过程联系起来来做到这一点,这更容易分析。我们还考虑了模型的其他性质,例如,当有一个巨型组件时,它是“稳定的”:对于一个典型的随机图,无论我们如何添加或删除o(n)边,巨型组件的大小变化不超过o(n)。

英文摘要:

We introduce a very general model of an inhomogenous random graph with independence between the edges, which scales so that the number of edges is linear in the number of vertices. This scaling corresponds to the p=c/n scaling for G(n,p) used to study the phase transition; also, it seems to be a property of many large real-world graphs. Our model includes as special cases many models previously studied. We show that under one very weak assumption (that the expected number of edges is `what it should be'), many properties of the model can be determined, in particular the critical point of the phase transition, and the size of the giant component above the transition. We do this by relating our random graphs to branching processes, which are much easier to analyze. We also consider other properties of the model, showing, for example, that when there is a giant component, it is `stable': for a typical random graph, no matter how we add or delete o(n) edges, the size of the giant component does not change by more than o(n).

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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