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Phase transitions for random geometric preferential attachment graphs.

机译:随机几何优先附着图的相变。

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摘要

Vertices arrive sequentially in space and are joined to existing vertices at random according to a preferential rule combining degree and spatial proximity. We investigate phase transitions in the resulting graph as the relative strengths of these two components of the attachment rule are varied. Previous work of one of the authors showed that when the geometric component is weak, the limiting degree sequence mimics the standard Barabási-Albert preferential attachment model. We show that at the other extreme, in the case of a sufficiently strong geometric component, the limiting degree sequence mimics a purely geometric model, the on-line nearest-neighbour graph, for which we prove some extensions of known results. We also show the presence of an intermediate regime, with behaviour distinct from both the on-line nearest-neighbour graph and the Barabási-Albert model; in this regime, we obtain a stretched exponential upper bound on the degree sequence.
机译:顶点在空间中顺序到达,并根据结合程度和空间接近度的优先规则将它们随机连接到现有顶点。随着附着力规则这两个分量的相对强度的变化,我们在结果图中调查了相变。一位作者的先前工作表明,当几何分量较弱时,极限度序列类似于标准的Barabási-Albert优先依恋模型。我们表明,在另一个极端情况下,在足够强大的几何分量的情况下,极限度序列模拟了纯几何模型,即在线最近邻图,为此我们证明了已知结果的一些扩展。我们还显示了中间制度的存在,其行为不同于在线最近邻图和Barabási-Albert模型。在这种情况下,我们获得了度数序列的拉伸指数上限。

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