Eigenvector centrality
Measure of the influence of a node in a network / From Wikipedia, the free encyclopedia
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In graph theory, eigenvector centrality (also called eigencentrality or prestige score[1]) is a measure of the influence of a node in a connected network. Relative scores are assigned to all nodes in the network based on the concept that connections to high-scoring nodes contribute more to the score of the node in question than equal connections to low-scoring nodes. A high eigenvector score means that a node is connected to many nodes who themselves have high scores.[2][3]
Google's PageRank and the Katz centrality are variants of the eigenvector centrality.[4]