Vertex similarity in networks
Physics and Society
2007-05-23 v1 Disordered Systems and Neural Networks
Data Analysis, Statistics and Probability
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.
Keywords
Cite
@article{arxiv.physics/0510143,
title = {Vertex similarity in networks},
author = {E. A. Leicht and Petter Holme and M. E. J. Newman},
journal= {arXiv preprint arXiv:physics/0510143},
year = {2007}
}