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Journal of Statistical Mechanics: Theory and Experiment

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Fast unfolding of communities in large networks

Vincent D Blondel1, Jean-Loup Guillaume1,2, Renaud Lambiotte1,3 and Etienne Lefebvre1

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We propose a simple method to extract the community structure of large networks. Our method is a heuristic method that is based on modularity optimization. It is shown to outperform all other known community detection methods in terms of computation time. Moreover, the quality of the communities detected is very good, as measured by the so-called modularity. This is shown first by identifying language communities in a Belgian mobile phone network of 2 million customers and by analysing a web graph of 118 million nodes and more than one billion links. The accuracy of our algorithm is also verified on ad hoc modular networks.


Keywords

random graphs, networks

new applications of statistical mechanics

 

E-print Number: 0803.0476

Cited: by |

Refers: to

PACS

89.75.Hc Networks and genealogical trees

02.10.Ox Combinatorics; graph theory

89.65.Gh Economics; econophysics, financial markets, business and management

MSC

05C80 Random graphs

91D30 Social networks

Subjects

Mathematical physics

Statistical physics and nonlinear systems

Dates

Issue 10 (October 2008)

Received 18 April 2008, accepted for publication 3 September 2008

Published 9 October 2008

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  1. Fast unfolding of communities in large networks

    Vincent D Blondel et al J. Stat. Mech. (2008) P10008

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