Abstract
With the rapidly grown evidence that various systems in nature and society can be modeled as complex networks, community detection in networks becomes a hot research topic in many research fields. This paper proposes a new genetic algorithm for community detection. The algorithm uses the fundamental measure criterion modularity Q as the fitness function. A special locus-based adjacency encoding scheme is applied to represent the community partition. The encoding scheme is suitable for the community detection based on the reason that it determines the community number automatically and reduces the search space distinctly. In addition, the corresponding crossover and mutation operators are designed. The experiments in three aspects show that the algorithm is effective, efficient and steady.
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© 2009 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
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Shi, C., Wang, Y., Wu, B., Zhong, C. (2009). A New Genetic Algorithm for Community Detection. In: Zhou, J. (eds) Complex Sciences. Complex 2009. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 5. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02469-6_11
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DOI: https://doi.org/10.1007/978-3-642-02469-6_11
Publisher Name: Springer, Berlin, Heidelberg
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