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    The Deutsche Physikalische Gesellschaft (DPG) with a tradition extending back to 1845 is the largest physical society in the world with more than 61,000 members. The DPG sees itself as the forum and mouthpiece for physics and is a non-profit organisation that does not pursue financial interests. It supports the sharing of ideas and thoughts within the scientific community, fosters physics teaching and would also like to open a window to physics for all those with a healthy curiosity.

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    The Institute of Physics (IOP) is a leading scientific society promoting physics and bringing physicists together for the benefit of all. It has a worldwide membership of around 40 000 comprising physicists from all sectors, as well as those with an interest in physics. It works to advance physics research, application and education; and engages with policy makers and the public to develop awareness and understanding of physics. Its publishing company, IOP Publishing, is a world leader in professional scientific communications.

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New Journal of Physics Volume 11 March 2009

Andrea Lancichinetti et al 2009 New J. Phys. 11 033015 doi:10.1088/1367-2630/11/3/033015

Detecting the overlapping and hierarchical community structure in complex networks

Andrea Lancichinetti1, Santo Fortunato1,3 and János Kertész2

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Abstract References Cited By Metrics

Many networks in nature, society and technology are characterized by a mesoscopic level of organization, with groups of nodes forming tightly connected units, called communities or modules, that are only weakly linked to each other. Uncovering this community structure is one of the most important problems in the field of complex networks. Networks often show a hierarchical organization, with communities embedded within other communities; moreover, nodes can be shared between different communities. Here, we present the first algorithm that finds both overlapping communities and the hierarchical structure. The method is based on the local optimization of a fitness function. Community structure is revealed by peaks in the fitness histogram. The resolution can be tuned by a parameter enabling different hierarchical levels of organization to be investigated. Tests on real and artificial networks give excellent results.


PACS

89.75.Hc Networks and genealogical trees

02.60.Pn Numerical optimization

89.75.Fb Structures and organization in complex systems

Subjects

Computational physics

Statistical physics and nonlinear systems

Dates

Issue 3 (March 2009)

Received 3 December 2008

Published 10 March 2009

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  1. Detecting the overlapping and hierarchical community structure in complex networks

    Andrea Lancichinetti et al 2009 New J. Phys. 11 033015

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