Optimisation of the coalescent hyperbolic embedding of complex networks

Abstract

Several observations indicate the existence of a latent hyperbolic space behind real networks that makes their structure very intuitive in the sense that the probability for a connection is decreasing with the hyperbolic distance between the nodes. A remarkable network model generating random graphs along this line is the popularity-similarity optimisation (PSO) model, offering a scale-free degree distribution, high clustering and the small world property at the same time. These results provide a strong motivation for the development of hyperbolic embedding algorithms, that tackle the problem of finding the optimal hyperbolic coordinates of the nodes based on the network structure. A very promising recent approach for hyperbolic embedding is provided by the noncentered minimum curvilinear embedding (ncMCE) method, belonging to the family of coalescent embedding algorithms. This approach offers a high quality embedding at a low running time. In the present work we propose a further optimisation of the angular coordinates in this framework that seems to reduce the logarithmic loss and increase the greedy routing score of the embedding compared to the original version, thereby adding an extra improvement to the quality of the inferred hyperbolic coordinates.

References in corpus (7)

• Fast unfolding of communities in large networks
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• Navigability of Complex Networks
• Sustaining the Internet with Hyperbolic Mapping
• Self-similarity of complex networks and hidden metric spaces
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Cited by in corpus (7)

• The inherent community structure of hyperbolic networks
• Model-independent methods for embedding directed networks into Euclidean and hyperbolic spaces
• Growing hyperbolic networks beyond two dimensions: the generalised popularity-similarity optimisation model
• Zoo Guide to Network Embedding
• Random graphs and real networks with weak geometric coupling
• Greedy routing optimisation in hyperbolic networks
• Hyperbolic embedding of multilayer networks