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Random subgraphs of a given graph.

机译:给定图的随机子图。

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摘要

In this thesis, we explore several problems related to understanding the relationships between a random subgraph of a host graph and the host graph itself, using the spectrum of the normalized Laplacian to understand the structure of both host graph and subgraph. In particular: (1) We study the emergence of the giant component in random subgraphs of a given graph. Under some relatively mild conditions on the degree sequence and spectral gap of the Laplacian, we show that if a graph G is percolated with probability p ≤ &parl0;1-e&parr0;d , where d˜ is the ratio of the second and first moments of the degree sequence, then the resulting subgraph Gp has no giant component asymptotically almost surely, while if p ≥ &parl0;1+e&parr0;d d˜ then there is a unique giant component with volume a constant fraction of the volume of the entire graph. This extends earlier work of Erdős and Renyi, who considered the problem with host graph Kn, and Frieze, Krivelevich and Martin who considered regular pseudo-random graphs, and others. (2) For a denser range of p, we exploit the spectral gap to show similarities between the structure of a random subgraph of a graph and the underlying host graph. The spectral gap of the normalized Laplacian yields a great deal of structural information about a graph, including discrepancy and expansion properties. We show that the spectral gap of a random subgraph is asymptotically the same as the spectral gap of host graph so long as pdmin " log3/2(n). (3) We also study another model of random subgraph, namely a uniformly randomly chosen spanning tree. We study the height of random spanning trees of general graphs; improving and generalizing earlier results of Aldous. Again, the spectral gap of the Laplacian is a key parameter in our understanding.
机译:在本文中,我们使用归一化拉普拉斯算子的频谱来理解主图和子图的结构,探讨了与理解主图的随机子图和主图本身之间的关系有关的几个问题。特别是:(1)我们研究给定图的随机子图中巨分量的出现。在拉普拉斯算子的度数序列和谱带隙的某些相对温和的条件下,我们表明,如果图G渗透概率为p≤&parl0; 1-e&parr0; d,其中d〜是第二矩和第一矩之比。如果按照度数序列,则所得子图Gp几乎肯定地没有渐近的巨成分,而如果p≥&parl; 1 + e&parr0; dd〜,则存在唯一的巨成分,其体积占整个图的体积的常数。这扩展了Erdő s和Renyi的早期工作,后者考虑了宿主图Kn的问题,而Frieze,Krivelevich和Martin考虑了规则伪随机图等问题。 (2)对于p的较密集范围,我们利用光谱间隙来显示图的随机子图的结构与基础主体图之间的相似性。归一化的拉普拉斯算子的谱隙产生了大量有关图的结构信息,包括差异和扩展性质。我们证明,只要pdmin“ log3 / 2(n),随机子图的光谱间隙就渐近地与主图的光谱间隙相同。(3)我们还研究了另一种随机子图模型,即均匀随机选择我们研究一般图的随机生成树的高度;改善和推广Aldous的早期结果;同样,拉普拉斯算子的谱隙是我们理解的关键参数。

著录项

  • 作者

    Horn, Paul Kenneth.;

  • 作者单位

    University of California, San Diego.;

  • 授予单位 University of California, San Diego.;
  • 学科 Mathematics.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 107 p.
  • 总页数 107
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 数学;
  • 关键词

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