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 of...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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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.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2021051 |
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| _version_ | 1850092367757967360 |
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| author | Jinyin CHEN Dunjie ZHANG Guohan HUANG Xiang LIN Liang BAO |
| author_facet | Jinyin CHEN Dunjie ZHANG Guohan HUANG Xiang LIN Liang BAO |
| author_sort | Jinyin CHEN |
| 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. |
| format | Article |
| id | doaj-art-e52ca536de0b487cbd12df43d3e17cc0 |
| institution | DOAJ |
| issn | 2096-109X |
| language | English |
| 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-e52ca536de0b487cbd12df43d3e17cc02025-08-20T02:42:08ZengPOSTS&TELECOM PRESS Co., LTD网络与信息安全学报2096-109X2021-06-01712859563329Adversarial attack and defense on graph neural networks: a surveyJinyin CHENDunjie ZHANGGuohan HUANGXiang LINLiang BAOFor 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.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2021051graph neural networksadversarial attackdefense algorithmsrobustness analysis |
| spellingShingle | Jinyin CHEN Dunjie ZHANG Guohan HUANG Xiang LIN Liang BAO 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.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2021051 |
| work_keys_str_mv | AT jinyinchen adversarialattackanddefenseongraphneuralnetworksasurvey AT dunjiezhang adversarialattackanddefenseongraphneuralnetworksasurvey AT guohanhuang adversarialattackanddefenseongraphneuralnetworksasurvey AT xianglin adversarialattackanddefenseongraphneuralnetworksasurvey AT liangbao adversarialattackanddefenseongraphneuralnetworksasurvey |