Reference : A Memory-Based Label Propagation Algorithm for Community Detection
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/38402
A Memory-Based Label Propagation Algorithm for Community Detection
English
Fiscarelli, Antonio Maria mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Brust, Matthias R. mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Danoy, Grégoire mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Bouvry, Pascal mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
2-Dec-2018
7
Complex Networks and Their Applications VII
Aiello, Luca Maria
Cherifi, Chantal
Cherifi, Hocine
Lambiotte, Renaud
Pietro, Liò
Rocha, Luis M.
Springer
171-182
Yes
International
978-3-030-05410-6
Cham
Switzerland
COMPLEX NETWORKS
from 11-12-2018 to 13-12-2018
Cambridge
United Kingdon
[en] Network Analysis ; Graph theory ; Community detection
[en] The objective of a community detection algorithm is to group
similar nodes in a network into communities, while increasing the dis-
similarity between them. Several methods have been proposed but many
of them are not suitable for large-scale networks because they have high
complexity and use global knowledge. The Label Propagation Algorithm
(LPA) assigns a unique label to every node and propagates the labels
locally, while applying the majority rule to reach a consensus. Nodes
which share the same label are then grouped into communities. Although
LPA excels with near linear execution time, it gets easily stuck in local
optima and often returns a single giant community. To overcome these
problems we propose MemLPA, a novel LPA where each node imple-
ments memory and the decision rule takes past states of the network
into account. We demonstrate through extensive experiments on the
Lancichinetti-Fortunato-Radicchi benchmark and a set of real-world net-
works that MemLPA outperforms most of state-of-the-art community
detection algorithms.
Luxembourg Center for Contemporary and Digital History (C2DH)
Fonds National de la Recherche - FnR
Researchers ; Professionals
http://hdl.handle.net/10993/38402
https://doi.org/10.1007/978-3-030-05411-3_14
FnR ; FNR10929115 > Andreas Fickers > DHH > Digital History and Hermeneutics > 01/03/2017 > 31/08/2023 > 2016

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