Paper Info
Title
Discrete Hyperbolic Random Graph Model.
Abstract
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 propose a discrete variant of the HRG model where nodes are mapped to the vertices of a triangulation; our algorithms allow us to work with this model in a simple yet efficient way. We present experimental results conducted on networks, both real-world and simulated, to evaluate the practical benefits of DHRG in comparison to the HRG model.
Year
DOI
Venue
2022
10.4230/LIPIcs.SEA.2022.1
Symposium on Experimental and Efficient Algorithms (SEA)
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Dorota Celińska-Kopczyńska100.34
Eryk Kopczynski2649.68