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

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

doi:10.3390/info12120530

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 journal › Article › peer-review

2 Citations (Scopus)

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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 language	English
Article number	530
Pages (from-to)	1-13
Number of pages	13
Journal	Information
Volume	12
Issue number	12
DOIs	https://doi.org/10.3390/info12120530
Publication status	Published (in print/issue) - 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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.3390/info12120530

Parallel Particle Swarm OptimizationFinal published version, 2.25 MBLicence: CC BY

Cite this

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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. {\textcopyright} 2021 by the authors. Licensee MDPI, Basel, Switzerland.",

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AU - Zhu, Tao

AU - Ning, Huansheng

AU - Chen, Luke

AU - Liu, Zhenyu

N1 - 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.

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KW - Parallel

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

Abstract

Bibliographical note

Keywords

UN SDGs

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