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Eigenvector centrality
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<p>In <a href="/facts/Graph_theory/VVPCd4TX">graph theory</a>, eigenvector centrality (also called eigencentrality or prestige score) is a measure of the influence of a <a href="/facts/Node_(networking)/LBp2vNcJ">node</a> in a connected <a href="/facts/Network_(mathematics)/kw3eIBUe">network</a>. 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. </p><p><a href="/facts/Google/GT9Sugza">Google</a>'s <a href="/facts/PageRank/KtJRJFUX">PageRank</a> and the <a href="/facts/Katz_centrality/DlyI7qdp">Katz centrality</a> are variants of the eigenvector centrality. </p>