Leicht, Elizabeth, Holme, Petter and Newman, Mark (2006) Vertex similarity in networks. Physical Review E (Statistical, Nonlinear, and Soft Matter Physics), 73 (2). pp. 1-10.
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:||Disordered Systems and Neural Networks; Data Analysis; Statistics and Probability|
|Centre:||CABDyN Complexity Centre|
|Date Deposited:||01 Feb 2012 19:41|
|Last Modified:||23 Oct 2015 14:07|
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