复杂网络中的模式检测：互联网的相关剖面
Pattern Detection in Complex Networks: Correlation Profile of the Internet
摘要：
提出了一种检测和分析大型复杂网络拓扑模式的通用方案。在这个方案中，网络的问题进行了比较，其适当的随机化版本，保留了一些低层次的拓扑性质。网络的任何可测量属性与这个空模型的统计显著偏差可能反映了它的设计原则和/或进化历史。我们说明了这个基本方案的例子互联网的相关性配置文件量化其相邻节点的连接之间的相关性。这种配置文件区分互联网从以前研究的分子网络具有类似的无标度连接分布。我们最后证明，在网络中的聚类是非常敏感的连接分布和相关性配置文件，并比较在互联网上的聚类适当的空模型。
英文摘要：
A general scheme for detecting and analyzing topological patterns in large complex networks is presented. In this scheme the network in question is compared with its properly randomized version that preserves some of its low-level topological properties. Statistically significant deviation of any measurable property of a network from this null model likely reflect its design principles and/or evolutionary history. We illustrate this basic scheme on the example of the correlation profile of the Internet quantifying correlations between connectivities of its neighboring nodes. This profile distinguishes the Internet from previously studied molecular networks with a similar scale-free connectivity distribution. We finally demonstrate that clustering in a network is very sensitive to both the connectivity distribution and its correlation profile and compare the clustering in the Internet to the appropriate null model.
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