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

Tinghuai Ma, Mingliang Yue, Jingjing Qu, Yuan Tian, Abdullah Al-Dhelaan and Mznah Al-Rodhaan

Physica A: Statistical Mechanics and its Applications, 2018, vol. 503, issue C, 366-378

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.

Keywords: Social network; Community detection; Label propagation; Spark; Probability; Similarity (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:503:y:2018:i:c:p:366-378

DOI: 10.1016/j.physa.2018.02.130

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