Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 8
2255-2260

Parallelized community detection algorithm based on Spark

Liu Dongjiang1,2
Li Jianhui1
1. Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China
2. University of Chinese Academy of Sciences, Beijing 100190, China
DOI: 10.19734/j.issn.1001-3695.2019.03.0053

Abstract

This paper focused on graph clustering and proposed a parallelized community detection algorithm based on Spark. This algorithm was based on sequence community detection algorithm using extremal optimization. The sequence algorithm tried to choose one vertex each time when adjust the clusters. So it would take long time to adjust the clusters. For this reason, the proposed algorithm adopted a new multi-vertices selection method. This method tried to calculate a threshold value and found all the vertices whose fitness value was smaller than the threshold value. Then the proposed algorithm also needed to change the category of these vertices; besides, as the proposed method toke the fitness values of all the vertices into consideration when tried to calculate the threshold, it needed to select limited number of vertices in order to avoid the influence of extremely large fitness values. So if the proposed method selected great amount of vertices, it would only keep part of them. At the same time, the proposed algorithm also adopted a new vertices filtering method. This method could reduce the volume of graph data efficiently. The results show that proposed algorithm takes shorter time than other parallelized algorithms for comparison. It means that the proposed algorithm runs relatively faster.

Foundation Support

国家重点研发计划资助项目(2016YFB1000600)
中国科学院战略性先导科技专项资肋项目(XDA06010307)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2019.03.0053
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 8
Section: Algorithm Research & Explore
Pages: 2255-2260
Serial Number: 1001-3695(2020)08-003-2255-06

Publish History

[2020-08-05] Printed Article

Cite This Article

刘东江, 黎建辉. 基于Spark的并行社区发现算法 [J]. 计算机应用研究, 2020, 37 (8): 2255-2260. (Liu Dongjiang, Li Jianhui. Parallelized community detection algorithm based on Spark [J]. Application Research of Computers, 2020, 37 (8): 2255-2260. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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