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Conference Papers Year : 2017

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

Rémy Cazabet
Pierre Borgnat
Pablo Jensen

Abstract

Null models have many applications on networks, from testing the significance of observations to the conception of algorithms such as community detection. They ususally 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 both on synthetic and collected networks.
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Dates and versions

hal-01500354 , version 1 (03-04-2017)

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Rémy Cazabet, Pierre Borgnat, Pablo Jensen. Enhancing Space-Aware Community Detection Using Degree Constrained Spatial Null Model. CompleNet 2017 - 8th Conference on Complex Networks, Mar 2017, Dubrovnik, Croatia. pp.26118 - 55, ⟨10.1007/978-3-319-54241-6_4⟩. ⟨hal-01500354⟩
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