  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.
