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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 8
2375-2381

Overlapping community detection algorithm using global and local mutual information of graph

Chen Yanbinga,b
Zhang Yinglonga
a. School of Physics & Information Engineering, b. School of Computer, Minnan Normal University, Zhangzhou Fujian 363000, China
DOI: 10.19734/j.issn.1001-3695.2022.12.0787

Abstract

Graph neural network shows its outstanding ability in learning node representation. However, in terms of community detection, most graph neural network models still use K-means to locate community centers. In order to overcome the disadvantage that K-means is not suitable for clustering in high-dimensional space, this paper proposed an overlapping community of glo-bal and local mutual information of joint graphs, which was a graph neural network for dealing with overlapping community detection problems. Firstly, it used the node vector representation to express the graph structure information more accurately by maximizing graph mutual information. Then, on the basis of using the graph structure to locate the community center, by maximizing the community mutual information, the vector representation distance between nodes belonging to the same community was closer and closer to the community center. Finally, it designed a target distribution to help the model solve the overlapping community detection task better. Through comprehensive experiments, it shows that overDGI has strong competitiveness compared with several existing benchmark algorithms in overlapping community division.

Foundation Support

国家自然科学基金资助项目(61762036)
福建省自然科学基金项目(2021J011007,2021J011008,2022J01916,2023J01922)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.12.0787
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 8
Section: Algorithm Research & Explore
Pages: 2375-2381
Serial Number: 1001-3695(2023)08-020-2375-07

Publish History

[2023-03-08] Accepted Paper
[2023-08-05] Printed Article

Cite This Article

陈燕兵, 张应龙. 联合图全局和局部互信息的重叠社团检测算法 [J]. 计算机应用研究, 2023, 40 (8): 2375-2381. (Chen Yanbing, Zhang Yinglong. Overlapping community detection algorithm using global and local mutual information of graph [J]. Application Research of Computers, 2023, 40 (8): 2375-2381. )

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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