A spectral algorithm with additive clustering for the recovery of overlapping communities in networks

Emilie Kaufmann 1 Thomas Bonald 2 Marc Lelarge 3, 4
1 SEQUEL - Sequential Learning
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
3 DYOGENE - Dynamics of Geometric Networks
DI-ENS - Département d'informatique de l'École normale supérieure, CNRS - Centre National de la Recherche Scientifique : UMR 8548, Inria de Paris
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.
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Journal articles
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https://hal.archives-ouvertes.fr/hal-01963868
Contributor : Marc Lelarge <>
Submitted on : Friday, December 21, 2018 - 4:26:33 PM
Last modification on : Wednesday, May 15, 2019 - 3:49:06 AM

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  • HAL Id : hal-01963868, version 1

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Emilie Kaufmann, Thomas Bonald, Marc Lelarge. A spectral algorithm with additive clustering for the recovery of overlapping communities in networks. Theoretical Computer Science, Elsevier, 2018, 742, pp.3-26. ⟨hal-01963868⟩

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