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On Computing the Gromov Hyperbolicity

On computing the Gromov hyperbolicity
Authors: Cohen, Nathann; Coudert, David; Lancin, Aurélien;

On Computing the Gromov Hyperbolicity

Abstract

The Gromov hyperbolicity is an important parameter for analyzing complex networks which expresses how the metric structure of a network looks like a tree. It is for instance used to provide bounds on the expected stretch of greedy-routing algorithms in Internet-like graphs. However, the best-known theoretical algorithm computing this parameter runs in O ( n 3.69 ) time, which is prohibitive for large-scale graphs. In this article, we propose an algorithm for determining the hyperbolicity of graphs with tens of thousands of nodes. Its running time depends on the distribution of distances and on the actual value of the hyperbolicity. Although its worst case runtime is O ( n 4 ), it is in practice much faster than previous proposals as observed in our experimentations. Finally, we propose a heuristic algorithm that can be used on graphs with millions of nodes. Our algorithms are all evaluated on benchmark instances.

Country
France
Keywords

[INFO.INFO-NI] Computer Science [cs]/Networking and Internet Architecture [cs.NI], Distance in graphs, [INFO.INFO-DS] Computer Science [cs]/Data Structures and Algorithms [cs.DS], algorithms, Gromov Hyperbolicity, Gromov hyperbolicity, Graph theory (including graph drawing) in computer science, Graph algorithms (graph-theoretic aspects), networks, Networks, Small world graphs, complex networks (graph-theoretic aspects), Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.), Algorithms

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
11
Average
Average
Top 10%
Green
bronze