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Optimization and Application of Clustering Algorithm in Community Discovery

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Abstract

In order to make up for the defect that the traditional spectral clustering algorithm cannot determine the number of clusters and the time-consuming calculation, this paper studies and improves the spectral clustering algorithm. In complex community networks, the spectral clustering algorithm based on modularity optimization is chosen to find the number of communities. In addition, four types of user attribute information are integrated, and a more reasonable user similarity model is constructed. At the same time, the original non-parallelized spectral clustering algorithm is optimized, and its improved scheme is suitable for the application of distributed computing. Many Hadoop optimization strategies are proposed for virtual community discovery scenarios in large-scale communities. Finally, the experimental results show that the efficiency of the parallelized spectral clustering algorithm is greatly improved, which can be applied to the virtual community discovery in large-scale social networks.

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Acknowledgements

The authors acknowledge the National Natural Science Foundation of China (Grant: 61673248), the Natural Science Foundation of Shanxi Province (Grant: 201601D102030).

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Authors and Affiliations

  1. School of Computer and Information Technology, Shanxi University, Taiyuan, China

    Hu Zhang & Yongke Wu

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  1. Hu Zhang
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  2. Yongke Wu
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Correspondence to Hu Zhang.

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Zhang, H., Wu, Y. Optimization and Application of Clustering Algorithm in Community Discovery. Wireless Pers Commun 102, 2443–2454 (2018). https://doi.org/10.1007/s11277-018-5264-x

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  • DOI: https://doi.org/10.1007/s11277-018-5264-x

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