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• W2068771798 startingPage "e86891" @default.
• W2068771798 abstract "The detection of evolving communities in dynamic complex networks is a challenging problem that recently received attention from the research community. Dynamics clearly add another complexity dimension to the difficult task of community detection. Methods should be able to detect changes in the network structure and produce a set of community structures corresponding to different timestamps and reflecting the evolution in time of network data. We propose a novel approach based on game theory elements and extremal optimization to address dynamic communities detection. Thus, the problem is formulated as a mathematical game in which nodes take the role of players that seek to choose a community that maximizes their profit viewed as a fitness function. Numerical results obtained for both synthetic and real-world networks illustrate the competitive performance of this game theoretical approach." @default.
• W2068771798 created "2016-06-24" @default.
• W2068771798 creator A5009798916 @default.
• W2068771798 creator A5081542613 @default.
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• W2068771798 date "2014-02-26" @default.
• W2068771798 modified "2023-09-25" @default.
• W2068771798 title "Game Theory and Extremal Optimization for Community Detection in Complex Dynamic Networks" @default.
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• W2068771798 doi "https://doi.org/10.1371/journal.pone.0086891" @default.
• W2068771798 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3935827" @default.
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• W2068771798 hasPublicationYear "2014" @default.
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