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Enhancing Space-Aware Community Detection Using Degree Constrained Spatial Null Model

  • Remy CazabetEmail author
  • Pierre Borgnat
  • Pablo Jensen
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)

Abstract

Null models have many applications on networks, from testing the significance of observations to the conception of algorithms such as community detection. They usually preserve some network properties, such as degree distribution. Recently, some null-models have been proposed for spatial networks, and applied to the community detection problem. In this article, we propose a new null-model adapted to spatial networks, that, unlike previous ones, preserves both the spatial structure and the degrees of nodes. We show the efficacy of this null-model in the community detection case on synthetic networks.

Keywords

Null Model Community Detection Normalize Mutual Information Radiation Model Spatial Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This work is funded in part by the ANR Vel’Innov: Vel’Innov ANR-12-SOIN-0001-02, European Commission H2020 FETPROACT 2016–2017 program under grant 732942 (ODYCCEUS), by the ANR (French National Agency of Research) under grants ANR-15-CE38-0001 (AlgoDiv) and ANR-13-CORD-0017-01 (CODDDE), by the French program “PIA - Usages, services et contenus innovants” under grant O18062-44430 (REQUEST), and by the Ile-de-France program FUI21 under grant 16010629 (iTRAC).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Sorbonne Universites, UPMC Univ Paris 06ParisFrance
  2. 2.CNRS, Laboratoire de PhysiqueUniv Lyon, Ens de Lyon, Univ Claude BernardVilleurbanneFrance

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