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Adversarial immunization for improving certifiable robustness on graphs. (2020).  Shuchang Tao Huawei Shen Qi Cao Liang Hou and Xueqi Cheng. 2020. Adversarial immunization for improving certifiable robustness on graphs. (2020)."},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9054731"},{"key":"e_1_3_2_2_47_1","volume-title":"International Conference on Learning Representations.","author":"Petar Velivc","year":"2018","unstructured":"Petar Velivc kovi\u0107, Guillem Cucurull , Arantxa Casanova , Adriana Romero , Pietro Lio , and Yoshua Bengio . 2018 . Graph attention networks . In International Conference on Learning Representations. Petar Velivc kovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2018. Graph attention networks. 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Characterizing malicious edges targeting on graph neural networks. (2018).  Xiaojun Xu Yue Yu Bo Li Le Song Chengfeng Liu and Carl Gunter. 2018. Characterizing malicious edges targeting on graph neural networks. (2018)."},{"key":"e_1_3_2_2_57_1","volume-title":"International conference on machine learning. PMLR, 40--48","author":"Yang Zhilin","year":"2016","unstructured":"Zhilin Yang , William Cohen , and Ruslan Salakhudinov . 2016 . Revisiting semi-supervised learning with graph embeddings . In International conference on machine learning. PMLR, 40--48 . Zhilin Yang, William Cohen, and Ruslan Salakhudinov. 2016. Revisiting semi-supervised learning with graph embeddings. In International conference on machine learning. PMLR, 40--48."},{"key":"e_1_3_2_2_58_1","volume-title":"Spectral norm regularization for improving the generalizability of deep learning. arXiv preprint arXiv:1705.10941","author":"Yoshida Yuichi","year":"2017","unstructured":"Yuichi Yoshida and Takeru Miyato . 2017. 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In International Conference on Learning Representations."},{"key":"e_1_3_2_2_60_1","first-page":"9263","article-title":"GNNGuard: Defending graph neural networks against adversarial attacks","volume":"33","author":"Zhang Xiang","year":"2020","unstructured":"Xiang Zhang and Marinka Zitnik . 2020 . GNNGuard: Defending graph neural networks against adversarial attacks . Advances in Neural Information Processing Systems , Vol. 33 (2020), 9263 -- 9275 . Xiang Zhang and Marinka Zitnik. 2020. GNNGuard: Defending graph neural networks against adversarial attacks. Advances in Neural Information Processing Systems, Vol. 33 (2020), 9263--9275.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_61_1","volume-title":"International Conference on Learning Representations.","author":"Zhao Lingxiao","year":"2020","unstructured":"Lingxiao Zhao and Leman Akoglu . 2020 . Pairnorm: Tackling oversmoothing in GNNs . In International Conference on Learning Representations. Lingxiao Zhao and Leman Akoglu. 2020. Pairnorm: Tackling oversmoothing in GNNs. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_62_1","volume-title":"International Conference on Machine Learning. PMLR, 12719--12735","author":"Zhao Xin","year":"2021","unstructured":"Xin Zhao , Zeru Zhang , Zijie Zhang , Lingfei Wu , Jiayin Jin , Yang Zhou , Ruoming Jin , Dejing Dou , and Da Yan . 2021 . Expressive 1-lipschitz neural networks for robust multiple graph learning against adversarial attacks . In International Conference on Machine Learning. PMLR, 12719--12735 . Xin Zhao, Zeru Zhang, Zijie Zhang, Lingfei Wu, Jiayin Jin, Yang Zhou, Ruoming Jin, Dejing Dou, and Da Yan. 2021. Expressive 1-lipschitz neural networks for robust multiple graph learning against adversarial attacks. In International Conference on Machine Learning. PMLR, 12719--12735."},{"key":"e_1_3_2_2_63_1","first-page":"4917","article-title":"Towards deeper graph neural networks with differentiable group normalization","volume":"33","author":"Zhou Kaixiong","year":"2020","unstructured":"Kaixiong Zhou , Xiao Huang , Yuening Li , Daochen Zha , Rui Chen , and Xia Hu . 2020 . Towards deeper graph neural networks with differentiable group normalization . Advances in Neural Information Processing Systems , Vol. 33 (2020), 4917 -- 4928 . Kaixiong Zhou, Xiao Huang, Yuening Li, Daochen Zha, Rui Chen, and Xia Hu. 2020. Towards deeper graph neural networks with differentiable group normalization. Advances in Neural Information Processing Systems, Vol. 33 (2020), 4917--4928.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_64_1","volume-title":"Advances in Neural Information Processing Systems","volume":"34","author":"Zhou Kaixiong","year":"2021","unstructured":"Kaixiong Zhou , Xiao Huang , Daochen Zha , Rui Chen , Li Li , Soo-Hyun Choi , and Xia Hu . 2021 . Dirichlet energy constrained learning for deep graph neural networks . Advances in Neural Information Processing Systems , Vol. 34 (2021). Kaixiong Zhou, Xiao Huang, Daochen Zha, Rui Chen, Li Li, Soo-Hyun Choi, and Xia Hu. 2021. Dirichlet energy constrained learning for deep graph neural networks. Advances in Neural Information Processing Systems, Vol. 34 (2021)."},{"key":"e_1_3_2_2_65_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330851"},{"key":"e_1_3_2_2_66_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2019.2961812"},{"key":"e_1_3_2_2_67_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.acha.2019.06.003"},{"key":"e_1_3_2_2_68_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220078"},{"key":"e_1_3_2_2_69_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394520"},{"key":"e_1_3_2_2_70_1","volume-title":"International Conference on Learning Representations.","author":"Z\u00fcgner Daniel","year":"2019","unstructured":"Daniel Z\u00fcgner and Stephan G\u00fcnnemann . 2019 . Adversarial Attacks on Graph Neural Networks via Meta Learning . In International Conference on Learning Representations. Daniel Z\u00fcgner and Stephan G\u00fcnnemann. 2019. Adversarial Attacks on Graph Neural Networks via Meta Learning. 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