We present an efficient algorithm for computing distances in hyperbolic grids. We apply this algorithm to work efficiently with a discrete variant of the hyperbolic random graph model . This model is gaining popularity in the analysis of scale-free networks, which are ubiquitous in many fields, from social network analysis to biology. We present experimental results conducted on real world networks  .
The hyperbolic random graph model (HRG) has proven useful in the analysis of scale-free networks, which are ubiquitous in many fields, from social network analysis to biology. However, working with this model is algorithmically and conceptually challenging because of the nature of the distances in the hyperbolic plane. In this paper we study the algorithmic properties of regularly generated triangulations in the hyperbolic plane. We propose a discrete variant of the HRG model where nodes are mapped to the vertices of such a triangulation; our algorithms allow us to work with this model in a simple yet efficient way. We present experimental results conducted on real world networks to evaluate the practical benefits of DHRG in comparison to the HRG model .
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We present an efficient algorithm for computing distances in hyperbolic grids. We apply this algorithm to work efficiently with a discrete variant of the hyperbolic
The hyperbolic
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. This model is gaining popularity
(HRG) has proven useful
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We
However, working with this model is algorithmically and conceptually challenging because of the nature of the distances in the hyperbolic plane. In this paper we study the algorithmic properties of regularly generated triangulations in the hyperbolic plane. We propose a discrete variant of the HRG model where nodes are mapped to the vertices of such a triangulation; our algorithms allow us to work with this model in a simple yet efficient way. We
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to evaluate the practical benefits of DHRG in comparison to the HRG model