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PSPLPA: Probability and similarity based parallel label propagation algorithm on spark

Author

Listed:
  • Ma, Tinghuai
  • Yue, Mingliang
  • Qu, Jingjing
  • Tian, Yuan
  • Al-Dhelaan, Abdullah
  • Al-Rodhaan, Mznah

Abstract

With the rapid growth of social network, the cost of computation is increasing. Many existing algorithms are not suitable for the large-scale data. Apache Spark is an open-source cluster computing framework that empowers us to solve the problem of community detection in a cluster of computer. In this paper, we propose a novel label propagation algorithm on Spark, called PSPLPA (Probability and similarity based Parallel label propagation algorithm). PSPLPA employs a new label updating strategy using probability in the label propagation procedure during each iteration. First, weight calculation, which is based on k-shell, is integrated into the label initialization process. Second, parallel propagation steps are comprehensively proposed to utilize label probability efficiently. Third, randomness in label updating is significantly reduced via automatic label selection and similarity computation. Experiments conducted on artificial and real social networks demonstrate that the proposed algorithm exhibits high scalability and high accuracy.

Suggested Citation

  • Ma, Tinghuai & Yue, Mingliang & Qu, Jingjing & Tian, Yuan & Al-Dhelaan, Abdullah & Al-Rodhaan, Mznah, 2018. "PSPLPA: Probability and similarity based parallel label propagation algorithm on spark," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 366-378.
    • Handle: RePEc:eee:phsmap:v:503:y:2018:i:c:p:366-378
    DOI: 10.1016/j.physa.2018.02.130
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    References listed on IDEAS

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    1. Gert Sabidussi, 1966. "The centrality index of a graph," Psychometrika, Springer;The Psychometric Society, vol. 31(4), pages 581-603, December.
    2. Liu, Jian-Guo & Ren, Zhuo-Ming & Guo, Qiang, 2013. "Ranking the spreading influence in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(18), pages 4154-4159.
    3. Gao, Shuai & Ma, Jun & Chen, Zhumin & Wang, Guanghui & Xing, Changming, 2014. "Ranking the spreading ability of nodes in complex networks based on local structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 403(C), pages 130-147.
    4. Lou, Hao & Li, Shenghong & Zhao, Yuxin, 2013. "Detecting community structure using label propagation with weighted coherent neighborhood propinquity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(14), pages 3095-3105.
    5. Bae, Joonhyun & Kim, Sangwook, 2014. "Identifying and ranking influential spreaders in complex networks by neighborhood coreness," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 395(C), pages 549-559.
    6. Hou, Bonan & Yao, Yiping & Liao, Dongsheng, 2012. "Identifying all-around nodes for spreading dynamics in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(15), pages 4012-4017.
    7. Lin, Zhen & Zheng, Xiaolin & Xin, Nan & Chen, Deren, 2014. "CK-LPA: Efficient community detection algorithm based on label propagation with community kernel," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 386-399.
    8. Chen, Naiyue & Liu, Yun & Chen, Haiqiang & Cheng, Junjun, 2017. "Detecting communities in social networks using label propagation with information entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 788-798.
    Full references (including those not matched with items on IDEAS)

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