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- W4300113114 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." @default.
- W4300113114 created "2022-10-03" @default.
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- W4300113114 date "2016-06-30" @default.
- W4300113114 modified "2023-10-16" @default.
- W4300113114 title "Generating massive complex networks with hyperbolic geometry faster in practice" @default.
- W4300113114 doi "https://doi.org/10.48550/arxiv.1606.09481" @default.
- W4300113114 hasPublicationYear "2016" @default.
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