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Arman Hasanzadeh, Ehsan Hajiramezanali, Shahin Boluki, Mingyuan Zhou, Nick Duffield, Krishna Narayanan, and Xiaoning Qian. 2020. Bayesian graph neural networks with adaptive connection sampling. In International conference on machine learning. PMLR, 4094--4104."},{"key":"e_1_3_2_2_25_1","volume-title":"Drug repurposing for COVID-19 using graph neural network with genetic, mechanistic, and epidemiological validation. Research Square","author":"Hsieh Kanglin","year":"2020","unstructured":"Kanglin Hsieh , Yinyin Wang , Luyao Chen , Zhongming Zhao , Sean Savitz , Xiaoqian Jiang , Jing Tang , and Yejin Kim . 2020. Drug repurposing for COVID-19 using graph neural network with genetic, mechanistic, and epidemiological validation. Research Square ( 2020 ). Kanglin Hsieh, Yinyin Wang, Luyao Chen, Zhongming Zhao, Sean Savitz, Xiaoqian Jiang, Jing Tang, and Yejin Kim. 2020. Drug repurposing for COVID-19 using graph neural network with genetic, mechanistic, and epidemiological validation. 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Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)."},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2020.3032755"},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.07.006"},{"key":"e_1_3_2_2_35_1","volume-title":"Dividemix: Learning with noisy labels as semi-supervised learning. arXiv preprint arXiv:2002.07394","author":"Li Junnan","year":"2020","unstructured":"Junnan Li , Richard Socher , and Steven CH Hoi . 2020 . Dividemix: Learning with noisy labels as semi-supervised learning. arXiv preprint arXiv:2002.07394 (2020). Junnan Li, Richard Socher, and Steven CH Hoi. 2020. Dividemix: Learning with noisy labels as semi-supervised learning. arXiv preprint arXiv:2002.07394 (2020)."},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-75762-5_42"},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403076"},{"key":"e_1_3_2_2_38_1","volume-title":"International Conference on Machine Learning. PMLR.","author":"Liu Xiaorui","year":"2021","unstructured":"Xiaorui Liu , Wei Jin , Yao Ma , Yaxin Li , Hua Liu , Yiqi Wang , Ming Yan , and Jiliang Tang . 2021 . Elastic graph neural networks . In International Conference on Machine Learning. PMLR. Xiaorui Liu, Wei Jin, Yao Ma, Yaxin Li, Hua Liu, Yiqi Wang, Ming Yan, and Jiliang Tang. 2021. Elastic graph neural networks. In International Conference on Machine Learning. 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Advances in neural information processing systems","author":"Malinin Andrey","year":"2018","unstructured":"Andrey Malinin and Mark Gales . 2018. Predictive uncertainty estimation via prior networks. Advances in neural information processing systems , Vol. 31 ( 2018 ). Andrey Malinin and Mark Gales. 2018. Predictive uncertainty estimation via prior networks. Advances in neural information processing systems, Vol. 31 (2018)."},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1378\/chest.111.6.1718"},{"key":"e_1_3_2_2_44_1","volume-title":"Choong Jun Jin, and Tsuyoshi Murata","author":"Hoang","year":"2019","unstructured":"Hoang NT , Choong Jun Jin, and Tsuyoshi Murata . 2019 . Learning Graph Neural Networks with Noisy Labels . arXiv preprint arXiv:1905.01591 (2019). Hoang NT, Choong Jun Jin, and Tsuyoshi Murata. 2019. Learning Graph Neural Networks with Noisy Labels. arXiv preprint arXiv:1905.01591 (2019)."},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2021.108335"},{"key":"e_1_3_2_2_46_1","volume-title":"Dropedge: Towards deep graph convolutional networks on node classification. arXiv preprint arXiv:1907.10903","author":"Rong Yu","year":"2019","unstructured":"Yu Rong , Wenbing Huang , Tingyang Xu , and Junzhou Huang . 2019 . Dropedge: Towards deep graph convolutional networks on node classification. arXiv preprint arXiv:1907.10903 (2019). Yu Rong, Wenbing Huang, Tingyang Xu, and Junzhou Huang. 2019. Dropedge: Towards deep graph convolutional networks on node classification. arXiv preprint arXiv:1907.10903 (2019)."},{"key":"e_1_3_2_2_47_1","volume-title":"Node Feature Kernels Increase Graph Convolutional Network Robustness. arXiv preprint arXiv:2109.01785","author":"Amine Seddik Mohamed El","year":"2021","unstructured":"Mohamed El Amine Seddik , Changmin Wu , Johannes F Lutzeyer , and Michalis Vazirgiannis . 2021. Node Feature Kernels Increase Graph Convolutional Network Robustness. arXiv preprint arXiv:2109.01785 ( 2021 ). Mohamed El Amine Seddik, Changmin Wu, Johannes F Lutzeyer, and Michalis Vazirgiannis. 2021. Node Feature Kernels Increase Graph Convolutional Network Robustness. arXiv preprint arXiv:2109.01785 (2021)."},{"key":"e_1_3_2_2_48_1","volume-title":"Collective classification in network data. 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Advances in Neural Information Processing Systems, Vol. 34 (2021)."},{"key":"e_1_3_2_2_52_1","volume-title":"Adversarial attack and defense on graph data: A survey. arXiv preprint arXiv:1812.10528","author":"Sun Lichao","year":"2018","unstructured":"Lichao Sun , Yingtong Dou , Carl Yang , Ji Wang , Philip S Yu , and Bo Li. 2018. Adversarial attack and defense on graph data: A survey. arXiv preprint arXiv:1812.10528 ( 2018 ). Lichao Sun, Yingtong Dou, Carl Yang, Ji Wang, Philip S Yu, and Bo Li. 2018. Adversarial attack and defense on graph data: A survey. arXiv preprint arXiv:1812.10528 (2018)."},{"key":"e_1_3_2_2_53_1","volume-title":"International Conference on Machine Learning. PMLR, 9346--9355","author":"Tam Edric","year":"2020","unstructured":"Edric Tam and David Dunson . 2020 . Fiedler regularization: Learning neural networks with graph sparsity . In International Conference on Machine Learning. PMLR, 9346--9355 . Edric Tam and David Dunson. 2020. Fiedler regularization: Learning neural networks with graph sparsity. In International Conference on Machine Learning. PMLR, 9346--9355."},{"key":"e_1_3_2_2_54_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611974348.7"},{"key":"e_1_3_2_2_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371851"},{"key":"e_1_3_2_2_56_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01150"},{"key":"e_1_3_2_2_57_1","doi-asserted-by":"publisher","DOI":"10.1145\/2433396.2433461"},{"key":"e_1_3_2_2_58_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.60"},{"key":"e_1_3_2_2_59_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2007.190672"},{"key":"e_1_3_2_2_60_1","volume-title":"Unifying graph convolutional neural networks and label propagation. arXiv preprint arXiv:2002.06755","author":"Wang Hongwei","year":"2020","unstructured":"Hongwei Wang and Jure Leskovec . 2020. Unifying graph convolutional neural networks and label propagation. arXiv preprint arXiv:2002.06755 ( 2020 ). Hongwei Wang and Jure Leskovec. 2020. Unifying graph convolutional neural networks and label propagation. arXiv preprint arXiv:2002.06755 (2020)."},{"key":"e_1_3_2_2_61_1","unstructured":"Xiaoyun Wang Joe Eaton Cho-Jui Hsieh and Felix Wu. 2018. Attack graph convolutional networks by adding fake nodes. arXiv preprint arXiv:1810.(2018).  Xiaoyun Wang Joe Eaton Cho-Jui Hsieh and Felix Wu. 2018. Attack graph convolutional networks by adding fake nodes. arXiv preprint arXiv:1810.(2018)."},{"key":"e_1_3_2_2_62_1","volume-title":"Adversarial examples on graph data: Deep insights into attack and defense. arXiv preprint arXiv:1903.01610","author":"Wu Huijun","year":"2019","unstructured":"Huijun Wu , Chen Wang , Yuriy Tyshetskiy , Andrew Docherty , Kai Lu , and Liming Zhu . 2019. Adversarial examples on graph data: Deep insights into attack and defense. arXiv preprint arXiv:1903.01610 ( 2019 ). Huijun Wu, Chen Wang, Yuriy Tyshetskiy, Andrew Docherty, Kai Lu, and Liming Zhu. 2019. Adversarial examples on graph data: Deep insights into attack and defense. arXiv preprint arXiv:1903.01610 (2019)."},{"key":"e_1_3_2_2_63_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482299"},{"key":"e_1_3_2_2_64_1","volume-title":"Unsupervised adversarially-robust representation learning on graphs. arXiv preprint arXiv:2012.02486","author":"Xu Jiarong","year":"2020","unstructured":"Jiarong Xu , Yang Yang , Junru Chen , Xin Jiang , Chunping Wang , Jiangang Lu , and Yizhou Sun . 2020b. Unsupervised adversarially-robust representation learning on graphs. arXiv preprint arXiv:2012.02486 ( 2020 ). Jiarong Xu, Yang Yang, Junru Chen, Xin Jiang, Chunping Wang, Jiangang Lu, and Yizhou Sun. 2020b. Unsupervised adversarially-robust representation learning on graphs. arXiv preprint arXiv:2012.02486 (2020)."},{"key":"e_1_3_2_2_65_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9054465"},{"key":"e_1_3_2_2_66_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3072345"},{"key":"e_1_3_2_2_67_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219890"},{"key":"e_1_3_2_2_68_1","volume-title":"Gnnguard: Defending graph neural networks against adversarial attacks. arXiv preprint arXiv:2006.08149","author":"Zhang Xiang","year":"2020","unstructured":"Xiang Zhang and Marinka Zitnik . 2020 . Gnnguard: Defending graph neural networks against adversarial attacks. arXiv preprint arXiv:2006.08149 (2020). Xiang Zhang and Marinka Zitnik. 2020. Gnnguard: Defending graph neural networks against adversarial attacks. arXiv preprint arXiv:2006.08149 (2020)."},{"key":"e_1_3_2_2_69_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33015829"},{"key":"e_1_3_2_2_70_1","first-page":"12827","article-title":"Uncertainty aware semi-supervised learning on graph data","volume":"33","author":"Zhao Xujiang","year":"2020","unstructured":"Xujiang Zhao , Feng Chen , Shu Hu , and Jin-Hee Cho . 2020 . Uncertainty aware semi-supervised learning on graph data . Advances in Neural Information Processing Systems , Vol. 33 (2020), 12827 -- 12836 . Xujiang Zhao, Feng Chen, Shu Hu, and Jin-Hee Cho. 2020. Uncertainty aware semi-supervised learning on graph data. 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PMLR, 11458--11468","author":"Zheng Cheng","year":"2020","unstructured":"Cheng Zheng , Bo Zong , Wei Cheng , Dongjin Song , Jingchao Ni , Wenchao Yu , Haifeng Chen , and Wei Wang . 2020 . Robust graph representation learning via neural sparsification . In International Conference on Machine Learning. PMLR, 11458--11468 . Cheng Zheng, Bo Zong, Wei Cheng, Dongjin Song, Jingchao Ni, Wenchao Yu, Haifeng Chen, and Wei Wang. 2020. Robust graph representation learning via neural sparsification. In International Conference on Machine Learning. PMLR, 11458--11468."},{"key":"e_1_3_2_2_73_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00133"},{"key":"e_1_3_2_2_74_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330851"},{"key":"e_1_3_2_2_75_1","first-page":"7793","article-title":"Beyond homophily in graph neural networks: Current limitations and effective designs","volume":"33","author":"Zhu Jiong","year":"2020","unstructured":"Jiong Zhu , Yujun Yan , Lingxiao Zhao , Mark Heimann , Leman Akoglu , and Danai Koutra . 2020 . Beyond homophily in graph neural networks: Current limitations and effective designs . Advances in Neural Information Processing Systems , Vol. 33 (2020), 7793 -- 7804 . Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, and Danai Koutra. 2020. Beyond homophily in graph neural networks: Current limitations and effective designs. Advances in Neural Information Processing Systems, Vol. 33 (2020), 7793--7804.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_76_1","unstructured":"Xiaojin Zhu and Zoubin Ghahramani. 2002. Learning from labeled and unlabeled data with label propagation. (2002).  Xiaojin Zhu and Zoubin Ghahramani. 2002. Learning from labeled and unlabeled data with label propagation. (2002)."},{"key":"e_1_3_2_2_77_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i4.20362"},{"key":"e_1_3_2_2_78_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611977172.68"},{"key":"e_1_3_2_2_79_1","volume-title":"How Does Bayesian Noisy Self-Supervision Defend Graph Convolutional Networks? Neural Processing Letters","author":"Zhuang Jun","year":"2022","unstructured":"Jun Zhuang and Mohammad Al Hasan . 2022. How Does Bayesian Noisy Self-Supervision Defend Graph Convolutional Networks? Neural Processing Letters ( 2022 ), 1--22. Jun Zhuang and Mohammad Al Hasan. 2022. How Does Bayesian Noisy Self-Supervision Defend Graph Convolutional Networks? Neural Processing Letters (2022), 1--22."},{"key":"e_1_3_2_2_80_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220078"},{"key":"e_1_3_2_2_81_1","volume-title":"Adversarial attacks on graph neural networks via meta learning. arXiv preprint arXiv:1902.08412","author":"Z\u00fcgner Daniel","year":"2019","unstructured":"Daniel Z\u00fcgner and Stephan G\u00fcnnemann . 2019. Adversarial attacks on graph neural networks via meta learning. arXiv preprint arXiv:1902.08412 ( 2019 ). Daniel Z\u00fcgner and Stephan G\u00fcnnemann. 2019. 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