Reconstruct the Hierarchical Structure in a Complex Network

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dc.creatorYang, Huijie
dc.creatorWang, Wenxu
dc.creatorZhou, Tao
dc.creatorang, Binghong
dc.creatorZhao, Fangcui
dc.date2005-08-03
dc.date.accessioned2026-07-07T05:55:37Z
dc.date.available2026-07-07T05:55:37Z
dc.descriptionA 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.
dc.description8 figures
dc.identifierhttps://arxiv.org/abs/physics/0508026
dc.identifierhttp://arxiv.org/abs/physics/0508026
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/87306
dc.subjectPhysics and Society
dc.subjectStatistical Mechanics
dc.subjectBiological Physics
dc.subjectMolecular Networks
dc.titleReconstruct the Hierarchical Structure in a Complex Network
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