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G-Mixup: Graph Data Augmentation for Graph Classification. arXiv preprint arXiv:2202.07179 ( 2022 ). Xiaotian Han, Zhimeng Jiang, Ninghao Liu, and Xia Hu. 2022. G-Mixup: Graph Data Augmentation for Graph Classification. arXiv preprint arXiv:2202.07179 (2022)."},{"key":"e_1_3_2_2_16_1","volume-title":"On Non-Random Missing Labels in Semi-Supervised Learning. In International Conference on Learning Representations.","author":"Hu Xinting","year":"2022","unstructured":"Xinting Hu , Yulei Niu , Chunyan Miao , Xian-Sheng Hua , and Hanwang Zhang . 2022 . On Non-Random Missing Labels in Semi-Supervised Learning. In International Conference on Learning Representations. Xinting Hu, Yulei Niu, Chunyan Miao, Xian-Sheng Hua, and Hanwang Zhang. 2022. On Non-Random Missing Labels in Semi-Supervised Learning. 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On the complexity of linear prediction: Risk bounds, margin bounds, and regularization. Advances in neural information processing systems , Vol. 21 ( 2008 ). Sham M Kakade, Karthik Sridharan, and Ambuj Tewari. 2008. On the complexity of linear prediction: Risk bounds, margin bounds, and regularization. Advances in neural information processing systems , Vol. 21 (2008)."},{"key":"e_1_3_2_2_19_1","first-page":"14567","article-title":"Distribution aligning refinery of pseudo-label for imbalanced semi-supervised learning","volume":"33","author":"Kim Jaehyung","year":"2020","unstructured":"Jaehyung Kim , Youngbum Hur , Sejun Park , Eunho Yang , Sung Ju Hwang , and Jinwoo Shin . 2020 . Distribution aligning refinery of pseudo-label for imbalanced semi-supervised learning . Advances in Neural Information Processing Systems , Vol. 33 (2020), 14567 -- 14579 . Jaehyung Kim, Youngbum Hur, Sejun Park, Eunho Yang, Sung Ju Hwang, and Jinwoo Shin. 2020. 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Understanding attention and generalization in graph neural networks. Advances in neural information processing systems , Vol. 32 ( 2019 ). Boris Knyazev, Graham W Taylor, and Mohamed Amer. 2019. Understanding attention and generalization in graph neural networks. Advances in neural information processing systems , Vol. 32 (2019)."},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.324"},{"key":"e_1_3_2_2_23_1","volume-title":"2023 a. Data-Centric Learning from Unlabeled Graphs with Diffusion Model. arXiv preprint arXiv:2303.10108","author":"Liu Gang","year":"2023","unstructured":"Gang Liu , Eric Inae , Tong Zhao , Jiaxin Xu , Tengfei Luo , and Meng Jiang . 2023 a. Data-Centric Learning from Unlabeled Graphs with Diffusion Model. arXiv preprint arXiv:2303.10108 ( 2023 ). Gang Liu, Eric Inae, Tong Zhao, Jiaxin Xu, Tengfei Luo, and Meng Jiang. 2023 a. 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ChEMBL: towards direct deposition of bioassay data. Nucleic acids research , Vol. 47, D1 (2019), D930--D940."},{"key":"e_1_3_2_2_29_1","volume-title":"International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=37nvvqkCo5","author":"Menon Aditya Krishna","year":"2021","unstructured":"Aditya Krishna Menon , Sadeep Jayasumana , Ankit Singh Rawat , Himanshu Jain , Andreas Veit , and Sanjiv Kumar . 2021 . Long-tail learning via logit adjustment . In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=37nvvqkCo5 Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh Rawat, Himanshu Jain, Andreas Veit, and Sanjiv Kumar. 2021. Long-tail learning via logit adjustment. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=37nvvqkCo5"},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2017.250"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00956"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/EIDWT.2011.13"},{"key":"e_1_3_2_2_33_1","volume-title":"Quantum chemistry structures and properties of 134 kilo molecules. Scientific data","author":"Ramakrishnan Raghunathan","year":"2014","unstructured":"Raghunathan Ramakrishnan , Pavlo O Dral , Matthias Rupp , and O Anatole Von Lilienfeld . 2014. Quantum chemistry structures and properties of 134 kilo molecules. Scientific data , Vol. 1 , 1 ( 2014 ), 1--7. Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, and O Anatole Von Lilienfeld. 2014. Quantum chemistry structures and properties of 134 kilo molecules. Scientific data, Vol. 1, 1 (2014), 1--7."},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00777"},{"key":"e_1_3_2_2_35_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_36_1","first-page":"596","article-title":"Fixmatch: Simplifying semi-supervised learning with consistency and confidence","volume":"33","author":"Sohn Kihyuk","year":"2020","unstructured":"Kihyuk Sohn , David Berthelot , Nicholas Carlini , Zizhao Zhang , Han Zhang , Colin A Raffel , Ekin Dogus Cubuk , Alexey Kurakin , and Chun-Liang Li . 2020 . Fixmatch: Simplifying semi-supervised learning with consistency and confidence . Advances in Neural Information Processing Systems , Vol. 33 (2020), 596 -- 608 . Kihyuk Sohn, David Berthelot, Nicholas Carlini, Zizhao Zhang, Han Zhang, Colin A Raffel, Ekin Dogus Cubuk, Alexey Kurakin, and Chun-Liang Li. 2020. Fixmatch: Simplifying semi-supervised learning with consistency and confidence. Advances in Neural Information Processing Systems , Vol. 33 (2020), 596--608.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_37_1","volume-title":"Mixture regression for covariate shift. Advances in neural information processing systems","author":"Sugiyama Masashi","year":"2006","unstructured":"Masashi Sugiyama and Amos J Storkey . 2006. Mixture regression for covariate shift. Advances in neural information processing systems , Vol. 19 ( 2006 ). Masashi Sugiyama and Amos J Storkey. 2006. Mixture regression for covariate shift. Advances in neural information processing systems , Vol. 19 (2006)."},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v30i1.10306"},{"key":"e_1_3_2_2_39_1","volume-title":"International Conference on Learning Representations.","author":"Sun Fan-Yun","year":"2020","unstructured":"Fan-Yun Sun , Jordon Hoffman , Vikas Verma , and Jian Tang . 2020 . InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization . In International Conference on Learning Representations. Fan-Yun Sun, Jordon Hoffman, Vikas Verma, and Jian Tang. 2020. InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_40_1","volume-title":"Advances in Neural Information Processing Systems","volume":"32","author":"Tagasovska Natasa","year":"2019","unstructured":"Natasa Tagasovska and David Lopez-Paz . 2019 . Single-model uncertainties for deep learning . Advances in Neural Information Processing Systems , Vol. 32 (2019). Natasa Tagasovska and David Lopez-Paz. 2019. Single-model uncertainties for deep learning. Advances in Neural Information Processing Systems , Vol. 32 (2019)."},{"key":"e_1_3_2_2_41_1","unstructured":"A Thornton L Robeson B Freeman and D Uhlmann. 2012. Polymer Gas Separation Membrane Database.  A Thornton L Robeson B Freeman and D Uhlmann. 2012. Polymer Gas Separation Membrane Database."},{"key":"e_1_3_2_2_42_1","first-page":"8101","article-title":"Posterior re-calibration for imbalanced datasets","volume":"33","author":"Tian Junjiao","year":"2020","unstructured":"Junjiao Tian , Yen-Cheng Liu , Nathaniel Glaser , Yen-Chang Hsu , and Zsolt Kira . 2020 . Posterior re-calibration for imbalanced datasets . Advances in Neural Information Processing Systems , Vol. 33 (2020), 8101 -- 8113 . Junjiao Tian, Yen-Cheng Liu, Nathaniel Glaser, Yen-Chang Hsu, and Zsolt Kira. 2020. Posterior re-calibration for imbalanced datasets. Advances in Neural Information Processing Systems , Vol. 33 (2020), 8101--8113.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_43_1","volume-title":"Graph Attention Networks. In International Conference on Learning Representations.","author":"Petar Velivc","year":"2018","unstructured":"Petar Velivc kovi\u0107 , Guillem Cucurull , Arantxa Casanova , Adriana Romero , Pietro Li\u00f2 , and Yoshua Bengio . 2018 . Graph Attention Networks. In International Conference on Learning Representations. Petar Velivc kovi\u0107 , Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li\u00f2, and Yoshua Bengio. 2018. Graph Attention Networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_44_1","volume-title":"International Conference on Machine Learning. PMLR, 6438--6447","author":"Verma Vikas","year":"2019","unstructured":"Vikas Verma , Alex Lamb , Christopher Beckham , Amir Najafi , Ioannis Mitliagkas , David Lopez-Paz , and Yoshua Bengio . 2019 . Manifold mixup: Better representations by interpolating hidden states . In International Conference on Machine Learning. PMLR, 6438--6447 . Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Ioannis Mitliagkas, David Lopez-Paz, and Yoshua Bengio. 2019. Manifold mixup: Better representations by interpolating hidden states. In International Conference on Machine Learning. PMLR, 6438--6447."},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449796"},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01071"},{"key":"e_1_3_2_2_47_1","unstructured":"Ying-Xin Wu Xiang Wang An Zhang Xiangnan He and Tat seng Chua. 2022. Discovering Invariant Rationales for Graph Neural Networks. In ICLR.  Ying-Xin Wu Xiang Wang An Zhang Xiangnan He and Tat seng Chua. 2022. Discovering Invariant Rationales for Graph Neural Networks. In ICLR."},{"key":"e_1_3_2_2_48_1","volume-title":"MoleculeNet: a benchmark for molecular machine learning. 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