of the influence a node has on a network. If a node is pointed to by many nodes (which also have high eigenvector centrality) then that node will have high eigenvector centrality. The earliest use of eigenvector centrality is by Edmund Landau in an 1895 paper on scoring chess tournaments. More recently, researchers across many fields have analyzed applications, manifestations, and extensions of eigenvector centrality in a variety of domains: Eigenvector centrality is the unique measure satisfying certain natural axioms for a ranking system. In neuroscience, the eigenvector centrality of a neuron in a model neural network has been found to correlate with its relative firing rate. Eigenvector centrality and related concepts have been used to model opinion influence in sociology and economics, as in the DeGroot learning model. The definition of eigenvector centrality has been extended to multiplex or multilayer networks. In a study using data from the Philippines, researchers showed how political candidates' families had disproportionately high eigenvector centrality in local intermarriage networks. Eigenvector centrality has been extensively applied to study economic outcomes, including cooperation in social networks. In economic public goods problems, a person's eigenvector centrality can be interpreted as how much that person's preferences influence an efficient social outcome. See also Centrality References Graph theory Network analysis