title: Vertex similarity in networks.
abstract: We consider methods for quantifying the similarity of vertices in networks. We propose a measure of similarity based on the concept that two vertices are similar if their immediate neighbors in the network are themselves similar. This leads to a self-consistent matrix formulation of similarity that can be evaluated iteratively using only a knowledge of the adjacency matrix of the network. We test our similarity measure on computer-generated networks for which the expected results are known, and on a number of real-world networks.
authors.affiliation: Department of Physics, University of Michigan, Ann Arbor, Michigan 48109, USA.
authors.forename: E A
authors.initials: EA
authors.lastname: Leicht
authors.forename: Petter
authors.initials: P
authors.lastname: Holme
authors.forename: M E J
authors.initials: ME
authors.lastname: Newman
mesh_terms: D000818:Animals; D003198:Computer Simulation; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D009415:Nerve Net / Q000502:physiology*; D015398:Signal Transduction / Q000502:physiology*; D012944:Social Support