Random evolution in massive graphs

W. Aiello, F. Graham, Linyuan Lu
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引用次数: 278

Abstract

Many massive graphs (such as the WWW graph and Call graphs) share certain universal characteristics which can be described by the so-called "power law." In this paper, we, examine three important aspects of power law graphs, (1) the evolution of power law graphs, (2) the asymmetry of in-degrees and out-degrees, (3) the "scale invariance" of power law graphs. In particular, we give three increasingly general directed graph models and one general undirected graph model for generating power law graphs by adding at most one node and possibly one or more edges at a time. We show that for any given edge density and desired power laws for in-degrees and out-degrees, not necessarily the same, the resulting graph will almost surely have the desired edge density and the power laws for the in-degrees and out-degrees. Our most general directed and undirected models include nearly all known power law evolution models as special cases. Finally, we show that our evolution models generate "scale invariant" graphs. We describe a method for scaling the time in our evolution model such that the power law of the degree sequences remains invariant.
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海量图中的随机进化
许多大型图(如WWW图和Call图)都具有某些普遍的特征,这些特征可以用所谓的“幂律”来描述。本文研究了幂律图的三个重要方面:(1)幂律图的演化,(2)幂律图的内外度不对称,(3)幂律图的“尺度不变性”。特别地,我们给出了三种越来越通用的有向图模型和一种通用的无向图模型,用于通过一次最多添加一个节点和可能的一个或多个边来生成幂律图。我们表明,对于任何给定的边密度和所需的幂律的进度数和出度数,不一定相同,所得到的图几乎肯定具有所需的边密度和进度数和出度数的幂律。我们最一般的有向和无向模型包括几乎所有已知的幂律演化模型作为特例。最后,我们展示了我们的进化模型生成“尺度不变”图。我们描述了一种在进化模型中缩放时间的方法,使度序列的幂律保持不变。
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