Abstract
Community structure property is indispensable to discover the potential functionality of complex systems. Community detection (community discovery) is a technology for revealing the behavior of nodes aggregation in complex networks. To uncover the community structure of networks in a fast and effective way, in this paper, we propose a novel memetic algorithm called memetic algorithm with population learning (MAPL) based on the optimization of modularity. The proposed MAPL consists of a new initialization method, which can improve the population quality and accelerate the convergence of the algorithm to the optimal solutions, genetic operations and a local search using population learning to guide the direction of the optimization process. Extensive experiments on both synthetic networks and real-world networks demonstrate that compared with the five classic algorithms, the proposed MAPL has effective performance on discovering the community structure of complex networks.
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Acknowledgement
This work was supported by the National Natural Science Foundation of China (Grant No. 61703256, 61806119), Natural Science Basic Research Plan in Shaanxi Province of China (Program No. 2017JQ6070) and the Fundamental Research Funds for the Central Universities (Program No. GK201803020).
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Sun, X., Sun, Y., Cheng, S., Bian, K., Liu, Z. (2021). Population Learning Based Memetic Algorithm for Community Detection in Complex Networks. In: Tan, Y., Shi, Y., Zomaya, A., Yan, H., Cai, J. (eds) Data Mining and Big Data. DMBD 2021. Communications in Computer and Information Science, vol 1454. Springer, Singapore. https://doi.org/10.1007/978-981-16-7502-7_29
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DOI: https://doi.org/10.1007/978-981-16-7502-7_29
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