In graph theory, eigenvector centrality (also called eigencentrality or prestige score) is a measure of the influence of a node in a 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. Google's PageRank and the Katz centrality are variants of the eigenvector centrality. Using the adjacency matrix to find eigenvector centrality For a given graph with vertices let be the adjacency matrix, i.e. if vertex is linked to vertex , and otherwise. The relative centrality score, , of vertex can be defined as: where is the set of neighbors of and is a constant. With a small rearrangement this can be rewritten in vector notation as the eigenvector equation In general, there will be many different eigenvalues for which a non-zero eigenvector solution exists. However, the additional requirement that all the entries in the eigenvector be non-negative implies (by the Perron–Frobenius theorem) that only the greatest eigenvalue results in the desired centrality measure. The component of the related eigenvector then gives the relative centrality score of the vertex in the network. The eigenvector is only defined up to a common factor, so only the ratios of the centralities of the vertices are well defined. To define an absolute score, one must normalise the eigenvector e.g. such that the sum over all vertices is 1 or the total number of vertices n. Power iteration is one of many eigenvalue algorithms that may be used to find this dominant eigenvector. Furthermore, this can be generalized so that the entries in A can be real numbers representing connection strengths, as in a stochastic matrix. Normalized eigenvector centrality scoring Google's PageRank is based on the normalized eigenvector centrality, or normalized prestige, combined with a random jump assumption. The PageRank of a node has recursive dependence on the PageRank of other nodes that point to it. The normalized adjacency matrix is defined as:where is the out-degree of node , or in vector form: , where is the vector of ones, and is the diagonal matrix of vector . is a row-stochastic matrix. The normalized eigenvector prestige score is defined as: or in vector form, Applications Eigenvector centrality is a measure