Abstract
Community structure identification in complex networks has been an important research topic in recent years. Community detection can be viewed as an optimization problem in which an objective quality function that captures the intuition of a community as a group of nodes with better internal connectivity than external connectivity is chosen to be optimized. In this work Artificial bee colony (ABC) optimization has been used as an effective optimization technique to solve the community detection problem with the advantage that the number of communities is automatically determined in the process. However, the algorithm performance is influenced directly by the quality function used in the optimization process. A comparison is conducted between different popular communities’ quality measures when used as an objective function within ABC. Experiments on real life networks show the capability of the ABC to successfully find an optimized community structure based on the quality function used.
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Hafez, A.I., Zawbaa, H.M., Hassanien, A.E., Fahmy, A.A. (2014). Networks Community Detection Using Artificial Bee Colony Swarm Optimization. In: Kömer, P., Abraham, A., Snášel, V. (eds) Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014. Advances in Intelligent Systems and Computing, vol 303. Springer, Cham. https://doi.org/10.1007/978-3-319-08156-4_23
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DOI: https://doi.org/10.1007/978-3-319-08156-4_23
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08155-7
Online ISBN: 978-3-319-08156-4
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