{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T22:44:53Z","timestamp":1783550693686,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":37,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T00:00:00Z","timestamp":1697846400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["2023A1515011050"],"award-info":[{"award-number":["2023A1515011050"]}]},{"name":"National Key R&D Program of China","award":["2022YFF0902500"],"award-info":[{"award-number":["2022YFF0902500"]}]},{"name":"Ant Research Program","award":["20210002"],"award-info":[{"award-number":["20210002"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,10,21]]},"DOI":"10.1145\/3583780.3614903","type":"proceedings-article","created":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T07:45:26Z","timestamp":1697874326000},"page":"1198-1207","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["GUARD: Graph Universal Adversarial Defense"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6405-1531","authenticated-orcid":false,"given":"Jintang","family":"Li","sequence":"first","affiliation":[{"name":"Sun Yat-sen University, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1361-5325","authenticated-orcid":false,"given":"Jie","family":"Liao","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2005-6058","authenticated-orcid":false,"given":"Ruofan","family":"Wu","sequence":"additional","affiliation":[{"name":"Ant Group, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9682-0672","authenticated-orcid":false,"given":"Liang","family":"Chen","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7878-4330","authenticated-orcid":false,"given":"Zibin","family":"Zheng","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4735-5488","authenticated-orcid":false,"given":"Jiawang","family":"Dan","sequence":"additional","affiliation":[{"name":"Ant Group, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8992-9833","authenticated-orcid":false,"given":"Changhua","family":"Meng","sequence":"additional","affiliation":[{"name":"Ant Group, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6159-619X","authenticated-orcid":false,"given":"Weiqiang","family":"Wang","sequence":"additional","affiliation":[{"name":"Ant Group, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,10,21]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Aleksandar Bojchevski and Stephan G\u00fc nnemann. 2019. Certifiable Robustness to Graph Perturbations. In NeurIPS. 8317--8328. Aleksandar Bojchevski and Stephan G\u00fc nnemann. 2019. Certifiable Robustness to Graph Perturbations. In NeurIPS. 8317--8328."},{"key":"e_1_3_2_1_2_1","volume-title":"Fast gradient attack on network embedding. arXiv preprint arXiv:1809.02797","author":"Chen Jinyin","year":"2018","unstructured":"Jinyin Chen , Yangyang Wu , Xuanheng Xu , Yixian Chen , Haibin Zheng , and Qi Xuan . 2018. Fast gradient attack on network embedding. arXiv preprint arXiv:1809.02797 ( 2018 ). Jinyin Chen, Yangyang Wu, Xuanheng Xu, Yixian Chen, Haibin Zheng, and Qi Xuan. 2018. Fast gradient attack on network embedding. arXiv preprint arXiv:1809.02797 (2018)."},{"key":"e_1_3_2_1_3_1","volume-title":"A Survey of Adversarial Learning on Graph. arXiv preprint arXiv:2003.05730","author":"Chen Liang","year":"2020","unstructured":"Liang Chen , Jintang Li , Jiaying Peng , Tao Xie , Zengxu Cao , Kun Xu , Xiangnan He , and Zibin Zheng . 2020. A Survey of Adversarial Learning on Graph. arXiv preprint arXiv:2003.05730 ( 2020 ). Liang Chen, Jintang Li, Jiaying Peng, Tao Xie, Zengxu Cao, Kun Xu, Xiangnan He, and Zibin Zheng. 2020. A Survey of Adversarial Learning on Graph. arXiv preprint arXiv:2003.05730 (2020)."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"crossref","unstructured":"Liang Chen Jintang Li Qibiao Peng Yang Liu Zibin Zheng and Carl Yang. 2021. Understanding Structural Vulnerability in Graph Convolutional Networks. In IJCAI Zhi-Hua Zhou (Ed.). 2249--2255. Liang Chen Jintang Li Qibiao Peng Yang Liu Zibin Zheng and Carl Yang. 2021. Understanding Structural Vulnerability in Graph Convolutional Networks. In IJCAI Zhi-Hua Zhou (Ed.). 2249--2255.","DOI":"10.24963\/ijcai.2021\/310"},{"key":"e_1_3_2_1_5_1","volume-title":"Yu","author":"Dou Yingtong","year":"2020","unstructured":"Yingtong Dou , Zhiwei Liu , Li Sun , Yutong Deng , Hao Peng , and Philip S . Yu . 2020 . Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters. In CIKM. 315--324. Yingtong Dou, Zhiwei Liu, Li Sun, Yutong Deng, Hao Peng, and Philip S. Yu. 2020. Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters. In CIKM. 315--324."},{"key":"e_1_3_2_1_6_1","volume-title":"Papalexakis","author":"Entezari Negin","year":"2020","unstructured":"Negin Entezari , Saba A. Al-Sayouri , Amirali Darvishzadeh , and Evangelos E . Papalexakis . 2020 . All You Need Is Low (Rank): Defending Against Adversarial Attacks on Graphs. In WSDM. 169--177. Negin Entezari, Saba A. Al-Sayouri, Amirali Darvishzadeh, and Evangelos E. Papalexakis. 2020. All You Need Is Low (Rank): Defending Against Adversarial Attacks on Graphs. In WSDM. 169--177."},{"key":"e_1_3_2_1_7_1","volume-title":"Aleksandar Bojchevski, and Stephan G\u00fc nnemann.","author":"Geisler Simon","year":"2021","unstructured":"Simon Geisler , Tobias Schmidt , Hakan Sirin , Daniel Z\u00fc gner , Aleksandar Bojchevski, and Stephan G\u00fc nnemann. 2021 . Robustness of Graph Neural Networks at Scale. In NeurIPS. 7637--7649. Simon Geisler, Tobias Schmidt, Hakan Sirin, Daniel Z\u00fc gner, Aleksandar Bojchevski, and Stephan G\u00fc nnemann. 2021. Robustness of Graph Neural Networks at Scale. In NeurIPS. 7637--7649."},{"key":"e_1_3_2_1_8_1","first-page":"1321","article-title":"On Calibration of Modern Neural Networks","volume":"70","author":"Guo Chuan","year":"2017","unstructured":"Chuan Guo , Geoff Pleiss , Yu Sun , and Kilian Q. Weinberger . 2017 . On Calibration of Modern Neural Networks . In ICML , Vol. 70. 1321 -- 1330 . Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger. 2017. On Calibration of Modern Neural Networks. In ICML, Vol. 70. 1321--1330.","journal-title":"ICML"},{"key":"e_1_3_2_1_9_1","unstructured":"William L. Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In NeurIPS. 1024--1034. William L. Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In NeurIPS. 1024--1034."},{"key":"e_1_3_2_1_10_1","unstructured":"Weihua Hu Matthias Fey Marinka Zitnik Yuxiao Dong Hongyu Ren Bowen Liu Michele Catasta and Jure Leskovec. 2020. Open Graph Benchmark: Datasets for Machine Learning on Graphs. In NeurIPS. Weihua Hu Matthias Fey Marinka Zitnik Yuxiao Dong Hongyu Ren Bowen Liu Michele Catasta and Jure Leskovec. 2020. Open Graph Benchmark: Datasets for Machine Learning on Graphs. In NeurIPS."},{"key":"e_1_3_2_1_11_1","volume-title":"Carnegie Mellon UniversityA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating Deepfakes. CoRR","author":"Huang Hao","year":"2021","unstructured":"Hao Huang , Yongtao Wang , Zhaoyu Chen , Yuheng Li , Zhi Tang , Wei Chu , Jingdong Chen , Weisi Lin , and Kai-Kuang Ma. 2021. Carnegie Mellon UniversityA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating Deepfakes. CoRR , Vol. abs\/ 2105 .10872 ( 2021 ). Hao Huang, Yongtao Wang, Zhaoyu Chen, Yuheng Li, Zhi Tang, Wei Chu, Jingdong Chen, Weisi Lin, and Kai-Kuang Ma. 2021. Carnegie Mellon UniversityA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating Deepfakes. CoRR, Vol. abs\/2105.10872 (2021)."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"crossref","unstructured":"Wei Jin Tyler Derr Yiqi Wang Yao Ma Zitao Liu and Jiliang Tang. 2021. Node Similarity Preserving Graph Convolutional Networks. In WSDM. 148--156. Wei Jin Tyler Derr Yiqi Wang Yao Ma Zitao Liu and Jiliang Tang. 2021. Node Similarity Preserving Graph Convolutional Networks. In WSDM. 148--156.","DOI":"10.1145\/3437963.3441735"},{"key":"e_1_3_2_1_13_1","volume-title":"Graph Structure Learning for Robust Graph Neural Networks","author":"Jin Wei","unstructured":"Wei Jin , Yao Ma , Xiaorui Liu , Xianfeng Tang , Suhang Wang , and Jiliang Tang . 2020. Graph Structure Learning for Robust Graph Neural Networks . In KDD. Association for Computing Machinery , 66--74. Wei Jin, Yao Ma, Xiaorui Liu, Xianfeng Tang, Suhang Wang, and Jiliang Tang. 2020. Graph Structure Learning for Robust Graph Neural Networks. In KDD. Association for Computing Machinery, 66--74."},{"key":"e_1_3_2_1_14_1","volume-title":"Kingma and Jimmy Ba","author":"Diederik","year":"2015","unstructured":"Diederik P. Kingma and Jimmy Ba . 2015 . Adam : A Method for Stochastic Optimization. In ICLR (Poster) . Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In ICLR (Poster)."},{"key":"e_1_3_2_1_15_1","volume-title":"Kipf and Max Welling","author":"Thomas","year":"2017","unstructured":"Thomas N. Kipf and Max Welling . 2017 . Semi-Supervised Classification with Graph Convolutional Networks. In ICLR. Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In ICLR."},{"key":"e_1_3_2_1_16_1","volume-title":"Spectral Adversarial Training for Robust Graph Neural Network. TKDE","author":"Li Jintang","year":"2022","unstructured":"Jintang Li , Jiaying Peng , Liang Chen , Zibin Zheng , Tingting Liang , and Qing Ling . 2022a. Spectral Adversarial Training for Robust Graph Neural Network. TKDE ( 2022 ), 1--14. https:\/\/doi.org\/10.1109\/TKDE.2022.3222207 10.1109\/TKDE.2022.3222207 Jintang Li, Jiaying Peng, Liang Chen, Zibin Zheng, Tingting Liang, and Qing Ling. 2022a. Spectral Adversarial Training for Robust Graph Neural Network. TKDE (2022), 1--14. https:\/\/doi.org\/10.1109\/TKDE.2022.3222207"},{"key":"e_1_3_2_1_17_1","volume-title":"Distribution Shift, and Adversarial Attack. CoRR","author":"Li Jintang","year":"2022","unstructured":"Jintang Li , Bingzhe Wu , Chengbin Hou , Guoji Fu , Yatao Bian , Liang Chen , and Junzhou Huang . 2022b. Recent Advances in Reliable Deep Graph Learning: Inherent Noise , Distribution Shift, and Adversarial Attack. CoRR , Vol. abs\/ 2202 .07114 ( 2022 ). Jintang Li, Bingzhe Wu, Chengbin Hou, Guoji Fu, Yatao Bian, Liang Chen, and Junzhou Huang. 2022b. Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attack. CoRR, Vol. abs\/2202.07114 (2022)."},{"key":"e_1_3_2_1_18_1","first-page":"82","article-title":"Adversarial Attack on Large Scale Graph","volume":"35","author":"Li Jintang","year":"2023","unstructured":"Jintang Li , Tao Xie , Chen Liang , Fenfang Xie , Xiangnan He , and Zibin Zheng . 2023 . Adversarial Attack on Large Scale Graph . TKDE , Vol. 35 , 1 (2023), 82 -- 95 . https:\/\/doi.org\/10.1109\/TKDE.2021.3078755 10.1109\/TKDE.2021.3078755 Jintang Li, Tao Xie, Chen Liang, Fenfang Xie, Xiangnan He, and Zibin Zheng. 2023. Adversarial Attack on Large Scale Graph. TKDE, Vol. 35, 1 (2023), 82--95. https:\/\/doi.org\/10.1109\/TKDE.2021.3078755","journal-title":"TKDE"},{"key":"e_1_3_2_1_19_1","unstructured":"Xiaorui Liu Wei Jin Yao Ma Yaxin Li Liu Hua Yiqi Wang Ming Yan and Jiliang Tang. 2021. Elastic Graph Neural Networks. In ICML. Xiaorui Liu Wei Jin Yao Ma Yaxin Li Liu Hua Yiqi Wang Ming Yan and Jiliang Tang. 2021. Elastic Graph Neural Networks. In ICML."},{"key":"e_1_3_2_1_20_1","volume-title":"Universal Adversarial Perturbations","author":"Moosavi-Dezfooli Seyed-Mohsen","unstructured":"Seyed-Mohsen Moosavi-Dezfooli , Alhussein Fawzi , Omar Fawzi , and Pascal Frossard . 2017. Universal Adversarial Perturbations . In CVPR. IEEE Computer Society , 86--94. Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, and Pascal Frossard. 2017. Universal Adversarial Perturbations. In CVPR. IEEE Computer Society, 86--94."},{"key":"e_1_3_2_1_21_1","volume-title":"Stephan G\u00fc nnemann, and Aleksandar Bojchevski","author":"Mujkanovic Felix","year":"2022","unstructured":"Felix Mujkanovic , Simon Geisler , Stephan G\u00fc nnemann, and Aleksandar Bojchevski . 2022 . Are Defenses for Graph Neural Networks Robust?. In NeurIPS. Felix Mujkanovic, Simon Geisler, Stephan G\u00fc nnemann, and Aleksandar Bojchevski. 2022. Are Defenses for Graph Neural Networks Robust?. In NeurIPS."},{"key":"e_1_3_2_1_22_1","volume-title":"PyTorch: An Imperative Style","author":"Paszke Adam","unstructured":"Adam Paszke , Sam Gross , Francisco Massa , Adam Lerer , James Bradbury , Gregory Chanan , Trevor Killeen , Zeming Lin , Natalia Gimelshein , Luca Antiga , Alban Desmaison , Andreas K\u00f6pf , Edward Z. Yang , Zachary DeVito , Martin Raison , Alykhan Tejani , Sasank Chilamkurthy , Benoit Steiner , Lu Fang , Junjie Bai , and Soumith Chintala . 2019. PyTorch: An Imperative Style , High-Performance Deep Learning Library . In NeurIPS. 8024--8035. Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas K\u00f6pf, Edward Z. Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019. PyTorch: An Imperative Style, High-Performance Deep Learning Library. In NeurIPS. 8024--8035."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v29i3.2157"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"crossref","unstructured":"Daixin Wang Yuan Qi Jianbin Lin Peng Cui Quanhui Jia Zhen Wang Yanming Fang Quan Yu Jun Zhou and Shuang Yang. 2019a. A Semi-Supervised Graph Attentive Network for Financial Fraud Detection. In ICDM. 598--607. Daixin Wang Yuan Qi Jianbin Lin Peng Cui Quanhui Jia Zhen Wang Yanming Fang Quan Yu Jun Zhou and Shuang Yang. 2019a. A Semi-Supervised Graph Attentive Network for Financial Fraud Detection. In ICDM. 598--607.","DOI":"10.1109\/ICDM.2019.00070"},{"key":"e_1_3_2_1_25_1","volume-title":"Highly-Performant Package for Graph Neural Networks. arXiv preprint arXiv:1909.01315","author":"Wang Minjie","year":"2019","unstructured":"Minjie Wang , Da Zheng , Zihao Ye , Quan Gan , Mufei Li , Xiang Song , Jinjing Zhou , Chao Ma , Lingfan Yu , Yu Gai , Tianjun Xiao , Tong He , George Karypis , Jinyang Li , and Zheng Zhang . 2019b. Deep Graph Library: A Graph-Centric , Highly-Performant Package for Graph Neural Networks. arXiv preprint arXiv:1909.01315 ( 2019 ). Minjie Wang, Da Zheng, Zihao Ye, Quan Gan, Mufei Li, Xiang Song, Jinjing Zhou, Chao Ma, Lingfan Yu, Yu Gai, Tianjun Xiao, Tong He, George Karypis, Jinyang Li, and Zheng Zhang. 2019b. Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks. arXiv preprint arXiv:1909.01315 (2019)."},{"key":"e_1_3_2_1_26_1","volume-title":"Weinberger","author":"Wu Felix","year":"2019","unstructured":"Felix Wu , Amauri H. Souza Jr ., Tianyi Zhang , Christopher Fifty , Tao Yu , and Kilian Q . Weinberger . 2019 a. Simplifying Graph Convolutional Networks. In ICML (Proceedings of Machine Learning Research , Vol. 97). PMLR, 6861-- 6871 . Felix Wu, Amauri H. Souza Jr., Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Q. Weinberger. 2019a. Simplifying Graph Convolutional Networks. In ICML (Proceedings of Machine Learning Research, Vol. 97). PMLR, 6861--6871."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"crossref","unstructured":"Huijun Wu Chen Wang Yuriy Tyshetskiy Andrew Docherty Kai Lu and Liming Zhu. 2019b. Adversarial Examples for Graph Data: Deep Insights into Attack and Defense. In IJCAI. 4816--4823. Huijun Wu Chen Wang Yuriy Tyshetskiy Andrew Docherty Kai Lu and Liming Zhu. 2019b. Adversarial Examples for Graph Data: Deep Insights into Attack and Defense. In IJCAI. 4816--4823.","DOI":"10.24963\/ijcai.2019\/669"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"crossref","unstructured":"Jiancan Wu Xiang Wang Fuli Feng Xiangnan He Liang Chen Jianxun Lian and Xing Xie. 2021. Self-supervised Graph Learning for Recommendation. In SIGIR. 726--735. Jiancan Wu Xiang Wang Fuli Feng Xiangnan He Liang Chen Jianxun Lian and Xing Xie. 2021. Self-supervised Graph Learning for Recommendation. In SIGIR. 726--735.","DOI":"10.1145\/3404835.3462862"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"crossref","unstructured":"Kaidi Xu Hongge Chen Sijia Liu Pin-Yu Chen Tsui-Wei Weng Mingyi Hong and Xue Lin. 2019. Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective. In IJCAI. 3961--3967. Kaidi Xu Hongge Chen Sijia Liu Pin-Yu Chen Tsui-Wei Weng Mingyi Hong and Xue Lin. 2019. Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective. In IJCAI. 3961--3967.","DOI":"10.24963\/ijcai.2019\/550"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"crossref","unstructured":"Xiao Zang Yi Xie Jie Chen and Bo Yuan. 2021. Graph Universal Adversarial Attacks: A Few Bad Actors Ruin Graph Learning Models. In IJCAI. 3328--3334. https:\/\/doi.org\/10.24963\/ijcai.2021\/458 10.24963\/ijcai.2021 Xiao Zang Yi Xie Jie Chen and Bo Yuan. 2021. Graph Universal Adversarial Attacks: A Few Bad Actors Ruin Graph Learning Models. In IJCAI. 3328--3334. https:\/\/doi.org\/10.24963\/ijcai.2021\/458","DOI":"10.24963\/ijcai.2021\/458"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"crossref","unstructured":"Chaoning Zhang Philipp Benz Chenguo Lin Adil Karjauv Jing Wu and In So Kweon. 2021. A Survey on Universal Adversarial Attack. In IJCAI. 4687--4694. Chaoning Zhang Philipp Benz Chenguo Lin Adil Karjauv Jing Wu and In So Kweon. 2021. A Survey on Universal Adversarial Attack. In IJCAI. 4687--4694.","DOI":"10.24963\/ijcai.2021\/635"},{"key":"e_1_3_2_1_32_1","unstructured":"Xiang Zhang and Marinka Zitnik. 2020. GNNGuard: Defending Graph Neural Networks against Adversarial Attacks. In NeurIPS. Xiang Zhang and Marinka Zitnik. 2020. GNNGuard: Defending Graph Neural Networks against Adversarial Attacks. In NeurIPS."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.2981333"},{"key":"e_1_3_2_1_34_1","volume-title":"CSGNN: Contrastive Self-Supervised Graph Neural Network for Molecular Interaction Prediction. In IJCAI. 3756--3763.","author":"Zhao Chengshuai","year":"2021","unstructured":"Chengshuai Zhao , Shuai Liu , Feng Huang , Shichao Liu , and Wen Zhang . 2021 . CSGNN: Contrastive Self-Supervised Graph Neural Network for Molecular Interaction Prediction. In IJCAI. 3756--3763. Chengshuai Zhao, Shuai Liu, Feng Huang, Shichao Liu, and Wen Zhang. 2021. CSGNN: Contrastive Self-Supervised Graph Neural Network for Molecular Interaction Prediction. In IJCAI. 3756--3763."},{"key":"e_1_3_2_1_35_1","unstructured":"Dingyuan Zhu Ziwei Zhang Peng Cui and Wenwu Zhu. 2019. Robust Graph Convolutional Networks Against Adversarial Attacks. In KDD. 1399--1407. Dingyuan Zhu Ziwei Zhang Peng Cui and Wenwu Zhu. 2019. Robust Graph Convolutional Networks Against Adversarial Attacks. In KDD. 1399--1407."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"crossref","unstructured":"Daniel Z\u00fcgner Amir Akbarnejad and Stephan G\u00fcnnemann. 2018. Adversarial Attacks on Neural Networks for Graph Data. In KDD. 2847--2856. Daniel Z\u00fcgner Amir Akbarnejad and Stephan G\u00fcnnemann. 2018. Adversarial Attacks on Neural Networks for Graph Data. In KDD. 2847--2856.","DOI":"10.1145\/3219819.3220078"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"crossref","unstructured":"Daniel Z\u00fcgner and Stephan G\u00fcnnemann. 2019. Adversarial Attacks on Graph Neural Networks via Meta Learning. In ICLR. Daniel Z\u00fcgner and Stephan G\u00fcnnemann. 2019. Adversarial Attacks on Graph Neural Networks via Meta Learning. In ICLR.","DOI":"10.24963\/ijcai.2019\/872"}],"event":{"name":"CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management","location":"Birmingham United Kingdom","acronym":"CIKM '23","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 32nd ACM International Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3583780.3614903","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3583780.3614903","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:36:43Z","timestamp":1750178203000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3583780.3614903"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,21]]},"references-count":37,"alternative-id":["10.1145\/3583780.3614903","10.1145\/3583780"],"URL":"https:\/\/doi.org\/10.1145\/3583780.3614903","relation":{},"subject":[],"published":{"date-parts":[[2023,10,21]]},"assertion":[{"value":"2023-10-21","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}