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具有给定期望度的随机图中的平均距离。

The average distances in random graphs with given expected degrees.

作者信息

Chung Fan, Lu Linyuan

机构信息

Department of Mathematics, University of California at San Diego, La Jolla, CA 92093-0112, USA.

出版信息

Proc Natl Acad Sci U S A. 2002 Dec 10;99(25):15879-82. doi: 10.1073/pnas.252631999. Epub 2002 Dec 4.

Abstract

Random graph theory is used to examine the "small-world phenomenon"; any two strangers are connected through a short chain of mutual acquaintances. We will show that for certain families of random graphs with given expected degrees the average distance is almost surely of order log nlog d, where d is the weighted average of the sum of squares of the expected degrees. Of particular interest are power law random graphs in which the number of vertices of degree k is proportional to 1kbeta for some fixed exponent beta. For the case of beta > 3, we prove that the average distance of the power law graphs is almost surely of order log nlog d. However, many Internet, social, and citation networks are power law graphs with exponents in the range 2 < beta < 3 for which the power law random graphs have average distance almost surely of order log log n, but have diameter of order log n (provided having some mild constraints for the average distance and maximum degree). In particular, these graphs contain a dense subgraph, which we call the core, having n(clog log n) vertices. Almost all vertices are within distance log log n of the core although there are vertices at distance log n from the core.

摘要

随机图论用于研究“小世界现象”;任意两个陌生人都通过一小串相互认识的人联系在一起。我们将证明,对于具有给定期望度的某些随机图族,平均距离几乎肯定是(\log n / \log d)的量级,其中(d)是期望度平方和的加权平均值。特别令人感兴趣的是幂律随机图,其中度为(k)的顶点数量与(1 / k^{\beta})成正比,(\beta)为某个固定指数。对于(\beta > 3)的情况,我们证明幂律图的平均距离几乎肯定是(\log n / \log d)的量级。然而,许多互联网、社交和引用网络都是幂律图,其指数在(2 < \beta < 3)范围内,对于这些幂律随机图,平均距离几乎肯定是(\log \log n)的量级,但直径是(\log n)的量级(前提是对平均距离和最大度有一些适度的限制)。特别地,这些图包含一个密集子图,我们称之为核心,它有(n(c\log \log n))个顶点。几乎所有顶点到核心的距离都在(\log \log n)以内,尽管存在一些顶点到核心的距离为(\log n)。

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