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Community detection in the textile-related trade network using a biased estimation of distribution algorithm

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Abstract

It is very important to discovery potential customers or groups for the traditional textile enterprises to enhance its’ market competitiveness. Aiming to discover the implicit characters of the textile-related trade system, A biased estimation of distribution algorithm was proposed to detect the community structure in this paper. This algorithm combined a biased search and a simulated annealing selections strategy which were used to improve both the convergence speed and the accuracy of the EDAs to discovery the communities structure for complex system by maximizing the modularity density. The biased search is efficient by taking into account an asymmetric similarity between any pairs of nodes in network according to the different characteristics and local environment of nodes. The proposed algorithm was applied to detect the community structure for a textile-related trade network with the scale-free character extracted from a set of textile companies by uniquely leveraging each node with economic behavior, and the result show that the algorithm is efficient and competent

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Acknowledgements

This research is supported by the Zhejiang Provincial Education Department Research Foundation of China under Grant No.Y201533771 and Y201636127, Zhejiang Provincial Natural Science Foundation of China under Grant No. LY16F020027 and No.LY15F020040, the Brand Major of higher vocational education in Guangdong Province under Grant No. 2016gzpp126, and Humanity and Social Science Youth foundation of Ministry of Education of China under Grant No. 15YJCZH088. National Natural Science Foundation of China (No. 61370185), Natural Science Foundation of Guangdong Province (S2013010013432, S2013010015940), Science and Technology Planning Project of Huizhou (2014B050013016, 2014B020004023).

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Authors and Affiliations

  1. College of Mathematics and Information Engineering, Jiaxing University, Zhejiang, 434023, China

    Fahong Yu, Meijia Chen, Kun Deng, Xiaoyun Xia, Huiming Gao & Feng He

  2. School of Electronics and Communication, Shenzhen institute of information technology, Shenzhen, China

    Bolin Yu

  3. Ningbo Institute of Technology, Zhejiang University, Ningbo, China

    Longhua Ma

  4. Huizhou University, Huizhou Guangdong, China

    Zhao-Quan Cai

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  1. Fahong Yu
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  2. Meijia Chen
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  3. Kun Deng
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  4. Xiaoyun Xia
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  5. Bolin Yu
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  6. Huiming Gao
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  7. Feng He
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  8. Longhua Ma
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  9. Zhao-Quan Cai
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Correspondence to Fahong Yu.

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Yu, F., Chen, M., Deng, K. et al. Community detection in the textile-related trade network using a biased estimation of distribution algorithm. J Ambient Intell Human Comput 15, 1307–1316 (2024). https://doi.org/10.1007/s12652-017-0489-1

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  • DOI: https://doi.org/10.1007/s12652-017-0489-1

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