Adversarial attack and defense on graph neural networks: a survey
For the numerous existing adversarial attack and defense methods on GNN, the main adversarial attack and defense algorithms of GNN were reviewed comprehensively, as well as robustness analysis techniques. Besides, the commonly used benchmark datasets and evaluation metrics in the security research o...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
POSTS&TELECOM PRESS Co., LTD
2021-06-01
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| Series: | 网络与信息安全学报 |
| Subjects: | |
| Online Access: | http://www.infocomm-journal.com/cjnis/CN/10.11959/j.issn.2096-109x.2021051 |
| _version_ | 1819120919333830656 |
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| author | CHEN Jinyin ZHANG Dunjie, HUANG Guohan, LIN Xiang BAO Liang |
| author_facet | CHEN Jinyin ZHANG Dunjie, HUANG Guohan, LIN Xiang BAO Liang |
| author_sort | CHEN Jinyin |
| collection | DOAJ |
| description | For the numerous existing adversarial attack and defense methods on GNN, the main adversarial attack and defense algorithms of GNN were reviewed comprehensively, as well as robustness analysis techniques. Besides, the commonly used benchmark datasets and evaluation metrics in the security research of GNN were introduced. In conclusion, some insights on the future research direction of adversarial attacks and the trend of development were put forward. |
| first_indexed | 2024-12-22T06:28:19Z |
| format | Article |
| id | doaj.art-2929d3705ffe4a6983555f9127bc6dc7 |
| institution | Directory Open Access Journal |
| issn | 2096-109X |
| language | English |
| last_indexed | 2024-12-22T06:28:19Z |
| publishDate | 2021-06-01 |
| publisher | POSTS&TELECOM PRESS Co., LTD |
| record_format | Article |
| series | 网络与信息安全学报 |
| spelling | doaj.art-2929d3705ffe4a6983555f9127bc6dc72022-12-21T18:35:46ZengPOSTS&TELECOM PRESS Co., LTD网络与信息安全学报2096-109X2021-06-017312810.11959/j.issn.2096-109x.2021051Adversarial attack and defense on graph neural networks: a surveyCHEN Jinyin0ZHANG Dunjie, HUANG Guohan, LIN Xiang1BAO Liang2Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou 310023, China ;The College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, ChinaThe College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, ChinaKey Lab of Information Network Security, Ministry of Public Security, Shanghai 200000, ChinaFor the numerous existing adversarial attack and defense methods on GNN, the main adversarial attack and defense algorithms of GNN were reviewed comprehensively, as well as robustness analysis techniques. Besides, the commonly used benchmark datasets and evaluation metrics in the security research of GNN were introduced. In conclusion, some insights on the future research direction of adversarial attacks and the trend of development were put forward.http://www.infocomm-journal.com/cjnis/CN/10.11959/j.issn.2096-109x.2021051graph neural networksadversarial attackdefense algorithmsrobustness analysis |
| spellingShingle | CHEN Jinyin ZHANG Dunjie, HUANG Guohan, LIN Xiang BAO Liang Adversarial attack and defense on graph neural networks: a survey 网络与信息安全学报 graph neural networks adversarial attack defense algorithms robustness analysis |
| title | Adversarial attack and defense on graph neural networks: a survey |
| title_full | Adversarial attack and defense on graph neural networks: a survey |
| title_fullStr | Adversarial attack and defense on graph neural networks: a survey |
| title_full_unstemmed | Adversarial attack and defense on graph neural networks: a survey |
| title_short | Adversarial attack and defense on graph neural networks: a survey |
| title_sort | adversarial attack and defense on graph neural networks a survey |
| topic | graph neural networks adversarial attack defense algorithms robustness analysis |
| url | http://www.infocomm-journal.com/cjnis/CN/10.11959/j.issn.2096-109x.2021051 |
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