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Random Graph Generators for Hyperbolic Community Structures

  • Saskia Metzler
  • Pauli Miettinen
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 812)

Abstract

Proper testing of graph mining algorithms, for example, algorithms for community detection, requires the capability of creating realistic random graphs. As our understanding of real graph communities evolves, so should the random graph generators evolve, too. In this work, we propose a random graph generator called HyGen that, unlike the existing random graph generators, is designed to preserve the community structure of real networks, especially the commonly observed hyperbolic intra-community connectivity structure. The generated graphs will also preserve the total degree distributions and clustering coefficients of the original graph without introducing too much determinism. In addition, we also propose realistic distributions for the parameters controlling the hyperbolic shape of the communities.

Keywords

Random graphs Graph generators Community detection Hyperbolic community structure 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Max Planck Institute for InformaticsSaarbrückenGermany
  2. 2.University of Eastern FinlandKuopioFinland

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