 Eigenvector centrality

For a finite undirected graph with at least one edge, eigenvector centrality assigns nonnegative vertex scores proportional to the sum of neighbouring scores: $v_i={\lambda }_{\max }^{-1}{\sum }_j{A}_{ij}{v}_j$. It uses a nonnegative eigenvector of the adjacency matrix for its largest positive eigenvalue. State the normalization, such as Euclidean norm one. For a three-leaf star graph, hub and leaf scores have ratio $\sqrt{3}$.

This is the family of algorithms to which PageRank