Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 8
2278-2283

Graph neural networks adversarial training with non-robust features

Cheng Qi
Zhu Hongliang
Xin Yang
Cyber Security, Beijing University of Posts & Telecommunications, Beijing 100876, China
DOI: 10.19734/j.issn.1001-3695.2022.01.0012

Abstract

Graph convolutional neural networks can distill the effective information of graph data through graph convolution. However, the graph convolutional neural network shows vulnerability to adversarial attack, which leads to the degradation of model performance. Adversarial training can be used to improve the robustness of neural networks. However, since the structure and node features of graphs are usually discrete, it is impossible to directly construct adversarial examples based on gradients. Therefore, distilling feature of graph data in the embedding space of models as adversarial examples can reduce the complexity of adversarial training. By using the idea of the idea of ensemble learning, this paper innovatively proposed an adversarial training method based on non-robust features distillation for graph convolution network, VDERG. The method constructed two graph convolution neural networks as sub models from the two types of features of topology and node attributes respectively. Sub models distilled non-robust features through embedding space and used these features to implement adversarial training. Finally, the method combined the embedding given by the two sub models as the nodes' vectors. Experimental results show that the adversarial training strategy improves the accuracy of graph convolution neural networks in clean data by 0.8% on average, and improves the accuracy by 6.91% at most under adversarial attack.

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.01.0012
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 8
Section: Algorithm Research & Explore
Pages: 2278-2283
Serial Number: 1001-3695(2022)08-006-2278-06

Publish History

[2022-03-16] Accepted Paper
[2022-08-05] Printed Article

Cite This Article

承琪, 朱洪亮, 辛阳. 基于非鲁棒特征的图卷积神经网络对抗训练方法 [J]. 计算机应用研究, 2022, 39 (8): 2278-2283. (Cheng Qi, Zhu Hongliang, Xin Yang. Graph neural networks adversarial training with non-robust features [J]. Application Research of Computers, 2022, 39 (8): 2278-2283. )

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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