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Community Discovery Algorithm Based on Parallel Recommendation in Cloud Computing

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Advanced Hybrid Information Processing (ADHIP 2020)

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Abstract

In the cloud computing environment, traditional social network community discovery algorithms have low accuracy in social network community discovery, leading to information waste, community overlap and low scalability, and unable to achieve ideal computing results. Therefore, a social network based on parallel recommendation is proposed. Network community discovery algorithm. By mining the candidate trusted user set, the number and composition of the community are obtained, and the communication units are divided into overlapping communities and non-overlapping communities according to the different numbers of communities belonging to the nodes in the network. Combining the mining of candidate trusted user sets and community division, social networking is realized Network community discovers and calculates. Experiments show that the algorithm improves the accuracy and stability of social network community discovery, and has good application value.

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Acknowledgments

T his work is supported by Second batch of school-level scientific research projects of Huali College of Guangdong University of Technology (project number: HLKY-2018-ZK-07)

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Authors and Affiliations

  1. Huali College Guangdong University of Technology, Guangzhou, 511325, China

    Jian-li Zhai & Fang Meng

Authors
  1. Jian-li Zhai
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  2. Fang Meng
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Corresponding author

Correspondence to Jian-li Zhai .

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Editors and Affiliations

  1. Hunan Normal University, Changsha, China

    Shuai Liu

  2. Hunan Normal University, Changsha, China

    Liyun Xia

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© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Zhai, Jl., Meng, F. (2021). Community Discovery Algorithm Based on Parallel Recommendation in Cloud Computing. In: Liu, S., Xia, L. (eds) Advanced Hybrid Information Processing. ADHIP 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 348. Springer, Cham. https://doi.org/10.1007/978-3-030-67874-6_18

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  • DOI: https://doi.org/10.1007/978-3-030-67874-6_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-67873-9

  • Online ISBN: 978-3-030-67874-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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