Consensus centrality ranking of nodes in complex networks: An application to the Chinese stock market

Ranking nodes by their spreading ability is a fundamental problem in complex networks. To rank nodes effectively and efficiently, a new comprehensive centrality ranking, called consensus centrality ranking, is proposed based on the concept of consensus analysis, which identify a consensus view. In the present paper, we use a MST method to construct the China’ s stock network based on the basis of cross correlation of stock-prices and compare four classic centrality rankings to this proposed ranking and analyze their application to the China’ s stock network constructed.