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Conference object . 2012
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https://doi.org/10.1007/978-3-...
Part of book or chapter of book . 2012 . Peer-reviewed
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Exploration and Exploitation Operators for Genetic Graph Clustering Algorithm

descriptionPublicationkeyboard_double_arrow_right Part of book or chapter of book , Article , Conference object 01 Jan 2012 Czech Republic Publisher:Springer Berlin Heidelberg
Authors: Kohout, J.; Neruda, R. (Roman);

doi: 10.1007/978-3-642-34624-8_10

handle: 11104/0007334

Exploration and Exploitation Operators for Genetic Graph Clustering Algorithm

Abstract

In this paper, two evolutionary algorithms for clustering in the domain of directed weighted graphs are proposed. Several genetic operators are analyzed with respect to maintaining the balance between exploration and exploitation properties. The approach is extensively tested on medium-sized random graphs.

Country
Czech Republic
Related Organizations
Keywords

social networks, genetic algorithms, graph clustering

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average