Based on cluster de-synchronization properties of phase
oscillators, we introduce an efficient method for the detection
and identification of modules in complex networks. The performance
of the algorithm is tested on computer generated and real-world
networks whose modular structure is already known or has been
studied by means of other methods. The algorithm attains a high
level of precision, especially when the modular units are very
mixed and hardly detectable by the other methods, with a
computational effort $O(KN)$ on a generic graph with $N$
nodes and $K$ links.
%0 Book Section
%1 statphys23_0621
%A Boccaletti, S.
%A Ivanchenko, M.
%A Latora, V.
%A Pluchino, A.
%A Rapisarda, A.
%B Abstract Book of the XXIII IUPAP International Conference on Statistical Physics
%C Genova, Italy
%D 2007
%E Pietronero, Luciano
%E Loreto, Vittorio
%E Zapperi, Stefano
%K networks statphys23 synchronization topic-11
%T Detection of Complex Networks Modularity by Dynamical Clustering
%U http://st23.statphys23.org/webservices/abstract/preview_pop.php?ID_PAPER=621
%X Based on cluster de-synchronization properties of phase
oscillators, we introduce an efficient method for the detection
and identification of modules in complex networks. The performance
of the algorithm is tested on computer generated and real-world
networks whose modular structure is already known or has been
studied by means of other methods. The algorithm attains a high
level of precision, especially when the modular units are very
mixed and hardly detectable by the other methods, with a
computational effort $O(KN)$ on a generic graph with $N$
nodes and $K$ links.
@incollection{statphys23_0621,
abstract = {Based on cluster de-synchronization properties of phase
oscillators, we introduce an efficient method for the detection
and identification of modules in complex networks. The performance
of the algorithm is tested on computer generated and real-world
networks whose modular structure is already known or has been
studied by means of other methods. The algorithm attains a high
level of precision, especially when the modular units are very
mixed and hardly detectable by the other methods, with a
computational effort ${\cal O}(KN)$ on a generic graph with $N$
nodes and $K$ links.},
added-at = {2007-06-20T10:16:09.000+0200},
address = {Genova, Italy},
author = {Boccaletti, S. and Ivanchenko, M. and Latora, V. and Pluchino, A. and Rapisarda, A.},
biburl = {https://www.bibsonomy.org/bibtex/2ff15516041fcb665fee9c04c8ab0f756/statphys23},
booktitle = {Abstract Book of the XXIII IUPAP International Conference on Statistical Physics},
editor = {Pietronero, Luciano and Loreto, Vittorio and Zapperi, Stefano},
interhash = {802018d10e9e56f5a0999dbeea2a211e},
intrahash = {ff15516041fcb665fee9c04c8ab0f756},
keywords = {networks statphys23 synchronization topic-11},
month = {9-13 July},
timestamp = {2007-06-20T10:16:25.000+0200},
title = {Detection of Complex Networks Modularity by Dynamical Clustering},
url = {http://st23.statphys23.org/webservices/abstract/preview_pop.php?ID_PAPER=621},
year = 2007
}