泛化Kronecker图以建立可搜索网络模型

E. Bodine, B. Hassibi, A. Wierman
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引用次数: 4

摘要

本文通过定义一个“距离”相关的Kronecker算子,描述了随机Kronecker图的一个扩展,该扩展提供了可搜索性所需的特殊结构。我们展示了Kronecker图的扩展如何生成几个现有的社交网络模型,如Watts-Strogatz小世界模型和Kleinberg的基于格子的模型。我们关注一个扩展超立方体的具体例子,让人想起最近提出的基于隐藏双曲度量空间的社交网络模型,并证明贪婪转发算法可以找到长度为O((log log n)2)的n个节点图的非常短的路径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Generalizing Kronecker graphs in order to model searchable networks
This paper describes an extension to stochastic Kronecker graphs that provides the special structure required for searchability, by defining a “distance”-dependent Kronecker operator. We show how this extension of Kronecker graphs can generate several existing social network models, such as the Watts-Strogatz small-world model and Kleinberg's lattice-based model. We focus on a specific example of an expanding hypercube, reminiscent of recently proposed social network models based on a hidden hyperbolic metric space, and prove that a greedy forwarding algorithm can find very short paths of length O((log log n)2) for graphs with n nodes.
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