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arXiv · 1606.09481

Generating massive complex networks with hyperbolic geometry faster in practice

Moritz von Looz·Mustafa Özdayi·Sören Laue·Henning Meyerhenke
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Abstract

Generative network models play an important role in algorithm development, scaling studies, network analysis, and realistic system benchmarks for graph data sets. The commonly used graph-based benchmark model R-MAT has some drawbacks concerning realism and the scaling behavior of network properties. A complex network model gaining considerable popularity builds random hyperbolic graphs, generated by distributing points within a disk in the hyperbolic plane and then adding edges between points whose hyperbolic distance is below a threshold. We present in this paper a fast generation algorithm for such graphs. Our experiments show that our new generator achieves speedup factors of 3-60 over the best previous implementation. One billion edges can now be generated in under one minute on a shared-memory workstation. Furthermore, we present a dynamic extension to model gradual network change, while preserving at each step the point position probabilities.

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Moritz von Looz, Mustafa Özdayi, Sören Laue, Henning Meyerhenke. 2016-06-30. Generating massive complex networks with hyperbolic geometry faster in practice. https://arxiv.org/abs/1606.09481

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