Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 6
1715-1720

Information diffusion prediction based on hypergraph attention mechanism and graph convolution network

Miao Chenxiang
Liu Xiaoyang
School of Computer Science & Engineering, Chongqing University of Technology, Chongqing 400054, China
DOI: 10.19734/j.issn.1001-3695.2022.10.0510

Abstract

Aiming at the lack of global dependency mining of users in traditional information prediction models research, this paper proposed an information diffusion prediction model based on hypergraph attention mechanism and graph convolution network(HGACN). Firstly, it constructed the subgraph of user social relationship, and obtained the subcascade sequence by sampling, and learnt the structural features of user social relationship by graph convolutional neural network. Secondly, considering the global dependence between users and cascades, it used the hypergraph attention mechanism(HGAT) to learn the interaction characteristics of users at different time intervals. Finally, it captured the learned user representation into the embedded module, and used the gating mechanism to fuse it to obtain a more expressive user representation, and used the multi-head attention mechanism with mask for information prediction. The experimental results on Twitter and other five datasets show that the proposed HGACN model is improved by 4.4% in hits@N and 2.2% in map@N, which are significantly better than the existing diffusion prediction models such as MS-HGAT, proving that the proposed HGACN model is reasonable and effective. It is of great significance for HGACN to monitor rumors and detect malicious accounts.

Foundation Support

重庆市教委人文社科重点项目(23SKGH247)
国家教育考试科研规划2021年度课题(GJK2021028)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.10.0510
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 6
Section: Algorithm Research & Explore
Pages: 1715-1720
Serial Number: 1001-3695(2023)06-018-1715-06

Publish History

[2023-01-13] Accepted Paper
[2023-06-05] Printed Article

Cite This Article

苗琛香, 刘小洋. 融合超图注意力机制与图卷积网络的信息扩散预测 [J]. 计算机应用研究, 2023, 40 (6): 1715-1720. (Miao Chenxiang, Liu Xiaoyang. Information diffusion prediction based on hypergraph attention mechanism and graph convolution network [J]. Application Research of Computers, 2023, 40 (6): 1715-1720. )

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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