复杂网络中层次结构的重构
Reconstruct the Hierarchical Structure in a Complex Network
摘要：
最近的一些工作集中在复杂网络的一些统计特性，如聚类，右偏度分布和社区，这是许多现实世界中的网络所共有的。在本文中，我们讨论了层次属性共享之间的大量网络。提出了一种基于特征向量中心性测度的复杂网络层次结构重构方法。它在圣菲研究所协作网络上进行了测试，该网络的结构众所周知。我们还将其应用于数学家合作网络和酵母蛋白质相互作用网络。该方法可以检测到显着的层次结构，在这些网络。
英文摘要：
A number of recent works have concentrated on a few statistical properties of complex networks, such as the clustering, the right-skewed degree distribution and the community, which are common to many real world networks. In this paper, we address the hierarchy property sharing among a large amount of networks. Based upon the eigenvector centrality (EC) measure, a method is proposed to reconstruct the hierarchical structure of a complex network. It is tested on the Santa Fe Institute collaboration network, whose structure is well known. We also apply it to a Mathematicians' collaboration network and the protein interaction network of Yeast. The method can detect significantly hierarchical structures in these networks.
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