Enhancing Space-Aware Community Detection Using Degree Constrained Spatial Null Model
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.
KeywordsNull Model Community Detection Normalize Mutual Information Radiation Model Spatial Network
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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