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Conference papers
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.
Cited literature [16 references]
https://hal.archives-ouvertes.fr/hal-01500354
Contributor : Lionel Tabourier <>
Submitted on : Monday, April 3, 2017 - 10:47:56 AM
Last modification on : Wednesday, November 20, 2019 - 2:59:37 AM
Document(s) archivé(s) le : Tuesday, July 4, 2017 - 12:30:18 PM
Contributor : Lionel Tabourier <>
Submitted on : Monday, April 3, 2017 - 10:47:56 AM
Last modification on : Wednesday, November 20, 2019 - 2:59:37 AM
Document(s) archivé(s) le : Tuesday, July 4, 2017 - 12:30:18 PM
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nullSpatialModel.pdf
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