Year: 2006
Type: article
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.
Source: Physical Review E
Authors: E. A. Leicht, Petter Holme, M. E. J. Newman
Institution: University of Michigan
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
Funders: National Science Foundation, James S. McDonnell Foundation, Wenner-Gren Foundation, University of Michigan, National Institutes of Health, Gillings School of Public Health, National Institute of Child Health and Human Development, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Carolina Population Center, University of North Carolina at Chapel Hill
OpenAlex ID: W2037705937