Fast approximation of eccentricities and distances in hyperbolic graphs
Résumé
We show that the eccentricities (and thus the centrality indices) of all vertices of a δ-hyperbolic graph G = (V, E) can be computed in linear time with an additive one-sided error of at most cδ, i.e., after a linear time preprocessing, for every vertex v of G one can compute in O(1) time an estimateê(v) of its eccentricity eccG(v) such that eccG(v) ≤ê(v) ≤ eccG(v) + cδ for a small constant c. We prove that every δ-hyperbolic graph G has a shortest path tree, constructible in linear time, such that for every vertex v of G, eccG(v) ≤ eccT (v) ≤ eccG(v) + cδ. These results are based on an interesting monotonicity property of the eccentricity function of hyperbolic graphs: the closer a vertex is to the center of G, the smaller its eccentricity is. We also show that the distance matrix of G with an additive one-sided error of at most c ′ δ can be computed in O(|V | 2 log 2 |V |) time, where c ′ < c is a small constant. Recent empirical studies show that many real-world graphs (including Internet application networks, web networks, collaboration networks, social networks, biological networks, and others) have small hyperbolicity. So, we analyze the performance of our algorithms for approximating centrality and distance matrix on a number of real-world networks. Our experimental results show that the obtained estimates are even better than the theoretical bounds.
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