Аннотация
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 to investigate different
hierarchical levels of organization. Tests on real and artificial networks give
excellent results.
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