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

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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Communication dans un congrès
CompleNet 2017 - 8th Conference on Complex Networks, Mar 2017, Dubrovnik, Croatia. CompleNet 2017 - 8th Conference on Complex Networks, 64, pp.26118 - 55, 〈10.1007/978-3-319-54241-6_4〉
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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. CompleNet 2017 - 8th Conference on Complex Networks, 64, pp.26118 - 55, 〈10.1007/978-3-319-54241-6_4〉. 〈hal-01500354〉

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