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Descripción
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| Evolutionary algorithms have been demonstrated to be very competitive in the community detection for complex networks. They, however, show poor scalability to large-scale networks due to the exponential increase of search space. In this paper, we suggest a network reduction-based multiobjective evolutionary algorithm for community detection in large-scale networks, where the size of the networks is recursively reduced as the evolution proceeds. In each reduction of the network, the local communities found by the elite individuals in the population are identified as nodes of the reduced network for further evolution, thereby considerably reducing the search space. A local community repairing strategy is also suggested to correct the misidentified nodes after each network reduction during the evolution. Experimental results on synthetic and real-world networks demonstrate the superiority of the proposed ? | |
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Internacional
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Si |
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JCR del ISI
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Si |
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Título de la revista
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Ieee Transactions on Cybernetics |
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ISSN
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2168-2267 |
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Factor de impacto JCR
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8,803 |
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Información de impacto
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Volumen
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DOI
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Número de revista
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