Parallel particle swarm optimization based on spark for academic paper co-authorship prediction

Congmin Yang, Tao Zhu, Huansheng Ning, Luke Chen, Zhenyu Liu

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
13 Downloads (Pure)

Abstract

The particle swarm optimization (PSO) algorithm has been widely used in various optimization problems. Although PSO has been successful in many fields, solving optimization problems in big data applications often requires processing of massive amounts of data, which cannot be handled by traditional PSO on a single machine. There have been several parallel PSO based on Spark, however they are almost proposed for solving numerical optimization problems, and few for big data optimization problems. In this paper, we propose a new Spark-based parallel PSO algorithm to predict the co-authorship of academic papers, which we formulate as an optimization problem from massive academic data. Experimental results show that the proposed parallel PSO can achieve good prediction accuracy. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Original languageEnglish
Article number530
Pages (from-to)1-13
Number of pages13
JournalInformation
Volume12
Issue number12
DOIs
Publication statusPublished - 20 Dec 2021

Bibliographical note

Funding Information:
Funding: This research was supported by the National Natural Science Foundation of China (No. 62006110) and the Natural Science Foundation of Hunan Province (No. 2019JJ50499).

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

  • Big data
  • Link prediction
  • Parallel
  • Particle swarm optimization (PSO)
  • Spark

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