us to quantify the topological contribution (which is why is called contribution centrality) of each node to the centrality of a given node, having more weight/relevance those nodes with greater dissimilarity, since these allow to the given node access to nodes that which themselves can not access directly. Is noteworthy that is non-negative because and are non-negative matrices, so we can use the Perron–Frobenius theorem to ensure that the above problem has a unique solution for λ = λmax with c non-negative, allowing us to infer the centrality of each node in the network. Therefore, the centrality of the i-th node is where is the number of the nodes in the network. Several dissimilarity measures and networks were tested in obtaining improved results in the studied cases. See also Alpha centrality Core–periphery structure Distance in graphs Notes and references Further reading Koschützki, D.; Lehmann, K. A.; Peeters, L.; Richter, S.; Tenfelde-Podehl, D. and Zlotowski, O. (2005) Centrality Indices. In Brandes, U. and Erlebach, T. (Eds.) Network Analysis: Methodological Foundations, pp. 16–61, LNCS 3418, Springer-Verlag. Graph theory Graph algorithms Algebraic graph theory Networks Network analysis Network theory Graph distance