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.
For instance, the structural similarity measure of REF is based on the assumption that similar nodes tend to be linked.
Vertex similarity in networks.
Phys. Rev. E 73, 026120 (2006)
Medicine Mathematics Physics