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Multi-objective evolutionary algorithm using problem-specific genetic operators for community detection in networks

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Abstract

Automatic network clustering is an important method for mining the meaningful communities of complex networks. Uncovered communities help to understand the potential system structure and functionality. Many algorithms that use multiple optimization criteria and optimize a population of solutions are difficult to apply to real systems because they suffer a long optimization process. In this paper, in order to accelerate the optimization process and to uncover multiple significant community structures more effectively, a multi-objective evolutionary algorithm is proposed and evaluated using problem-specific genetic mutation and group crossover, and problem-specific initialization. Since crossover operators mainly contribute to performance of genetic algorithms, more problem-specific group crossover operators are introduced and evaluated for intelligent evolution of population. The experiments on both artificial and real-world networks demonstrate that the proposed evolutionary algorithm with problem-specific genetic operations has effective performance on discovering the community structure of networks.

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Acknowledgements

This work was supported by the Slovenian Research Agency (Grant Numbers: P2-0041, J2-6764). We would like to express our deepest gratitude to the anonymous reviewers for their valuable suggestions and corrections of the paper.

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Authors and Affiliations

  1. Faculty of Electrical Engineering and Computer Science, Faculty of Natural Science and Mathematics, University of Maribor, Maribor, Slovenia

    Krista Rizman Žalik

  2. Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia

    Borut Žalik

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  1. Krista Rizman Žalik
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  2. Borut Žalik
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Correspondence to Krista Rizman Žalik.

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Žalik, K.R., Žalik, B. Multi-objective evolutionary algorithm using problem-specific genetic operators for community detection in networks. Neural Comput & Applic 30, 2907–2920 (2018). https://doi.org/10.1007/s00521-017-2884-0

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  • DOI: https://doi.org/10.1007/s00521-017-2884-0

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