A Spectral Algorithm with Additive Clustering for the Recovery of Overlapping Communities in Networks

Emilie Kaufmann 1, 2, 3 Thomas Bonald 4, 5 Marc Lelarge 5, 6
1 SEQUEL - Sequential Learning
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
6 DYOGENE - Dynamics of Geometric Networks
DI-ENS - Département d'informatique de l'École normale supérieure, ENS Paris - École normale supérieure - Paris, Inria Paris-Rocquencourt, CNRS - Centre National de la Recherche Scientifique : UMR8548
Abstract : This paper presents a novel spectral algorithm with additive clustering, designed to identify overlapping communities in networks. The algorithm is based on geometric properties of the spectrum of the expected adjacency matrix in a random graph model that we call stochastic blockmodel with overlap (SBMO). An adaptive version of the algorithm, that does not require the knowledge of the number of hidden communities, is proved to be consistent under the SBMO when the degrees in the graph are (slightly more than) logarithmic. The algorithm is shown to perform well on simulated data and on real-world graphs with known overlapping communities.
Type de document :
Communication dans un congrès
Ronald Ortner; Hans Ulrich Simon; Sandra Zilles. ALT 2016 - Algorithmic Learning Theory, Oct 2016, Bari, Italy. Springer, 9925, pp.355-370, 2016, Lecture Notes in Computer Science. 〈10.1007/978-3-319-46379-7_24〉
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https://hal.archives-ouvertes.fr/hal-01163147
Contributeur : Emilie Kaufmann <>
Soumis le : jeudi 19 mai 2016 - 19:49:59
Dernière modification le : vendredi 17 novembre 2017 - 08:50:20

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Emilie Kaufmann, Thomas Bonald, Marc Lelarge. A Spectral Algorithm with Additive Clustering for the Recovery of Overlapping Communities in Networks. Ronald Ortner; Hans Ulrich Simon; Sandra Zilles. ALT 2016 - Algorithmic Learning Theory, Oct 2016, Bari, Italy. Springer, 9925, pp.355-370, 2016, Lecture Notes in Computer Science. 〈10.1007/978-3-319-46379-7_24〉. 〈hal-01163147v2〉

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