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Deep variational information bottleneck. arXiv preprint arXiv:1612.00410 ( 2016 ). Alexander A Alemi, Ian Fischer, Joshua V Dillon, and Kevin Murphy. 2016. Deep variational information bottleneck. arXiv preprint arXiv:1612.00410 (2016)."},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3289600.3290967"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5720"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-015-0069-3"},{"key":"e_1_3_2_2_5_1","volume-title":"Advances in Neural Information Processing Systems","volume":"28","author":"Bareinboim Elias","year":"2015","unstructured":"Elias Bareinboim , Andrew Forney , and Judea Pearl . 2015 . Bandits with unobserved confounders: A causal approach . Advances in Neural Information Processing Systems , Vol. 28 (2015). Elias Bareinboim, Andrew Forney, and Judea Pearl. 2015. Bandits with unobserved confounders: A causal approach. Advances in Neural Information Processing Systems, Vol. 28 (2015)."},{"key":"e_1_3_2_2_6_1","volume-title":"A meta-transfer objective for learning to disentangle causal mechanisms. arXiv preprint arXiv:1901.10912","author":"Bengio Yoshua","year":"2019","unstructured":"Yoshua Bengio , Tristan Deleu , Nasim Rahaman , Rosemary Ke , S\u00e9bastien Lachapelle , Olexa Bilaniuk , Anirudh Goyal , and Christopher Pal . 2019. A meta-transfer objective for learning to disentangle causal mechanisms. arXiv preprint arXiv:1901.10912 ( 2019 ). Yoshua Bengio, Tristan Deleu, Nasim Rahaman, Rosemary Ke, S\u00e9bastien Lachapelle, Olexa Bilaniuk, Anirudh Goyal, and Christopher Pal. 2019. A meta-transfer objective for learning to disentangle causal mechanisms. arXiv preprint arXiv:1901.10912 (2019)."},{"key":"e_1_3_2_2_7_1","volume-title":"Compendium of chemical terminology","author":"Book Gold","year":"2014","unstructured":"Gold Book . 2014. Compendium of chemical terminology . International Union of Pure and Applied Chemistry , Vol . 528 ( 2014 ). Gold Book. 2014. Compendium of chemical terminology. International Union of Pure and Applied Chemistry, Vol. 528 (2014)."},{"key":"e_1_3_2_2_8_1","volume-title":"Nature","volume":"559","author":"Butler Keith T","year":"2018","unstructured":"Keith T Butler , Daniel W Davies , Hugh Cartwright , Olexandr Isayev , and Aron Walsh . 2018 . Machine learning for molecular and materials science . Nature , Vol. 559 , 7715 (2018), 547--555. Keith T Butler, Daniel W Davies, Hugh Cartwright, Olexandr Isayev, and Aron Walsh. 2018. Machine learning for molecular and materials science. Nature, Vol. 559, 7715 (2018), 547--555."},{"key":"e_1_3_2_2_9_1","volume-title":"Drug-drug adverse effect prediction with graph co-attention. arXiv preprint arXiv:1905.00534","author":"Deac Andreea","year":"2019","unstructured":"Andreea Deac , Yu-Hsiang Huang , Petar Veli\u010dkovi\u0107 , Pietro Li\u00f2 , and Jian Tang . 2019. 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Mathematical correlations for describing solute transfer into functionalized alkane solvents containing hydroxyl, ether, ester or ketone solvents. Fluid phase equilibria, Vol. 298, 1 (2010), 48--53."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/511446.511513"},{"key":"e_1_3_2_2_21_1","volume-title":"Learning deep representations by mutual information estimation and maximization. arXiv preprint arXiv:1808.06670","author":"Hjelm R Devon","year":"2018","unstructured":"R Devon Hjelm , Alex Fedorov , Samuel Lavoie-Marchildon , Karan Grewal , Phil Bachman , Adam Trischler , and Yoshua Bengio . 2018. Learning deep representations by mutual information estimation and maximization. arXiv preprint arXiv:1808.06670 ( 2018 ). R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio. 2018. Learning deep representations by mutual information estimation and maximization. arXiv preprint arXiv:1808.06670 (2018)."},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jmedchem.6b01437"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.pc.36.100185.003041"},{"key":"e_1_3_2_2_24_1","volume-title":"Categorical reparameterization with gumbel-softmax. arXiv preprint arXiv:1611.01144","author":"Jang Eric","year":"2016","unstructured":"Eric Jang , Shixiang Gu , and Ben Poole . 2016. Categorical reparameterization with gumbel-softmax. arXiv preprint arXiv:1611.01144 ( 2016 ). Eric Jang, Shixiang Gu, and Ben Poole. 2016. Categorical reparameterization with gumbel-softmax. arXiv preprint arXiv:1611.01144 (2016)."},{"key":"e_1_3_2_2_25_1","unstructured":"March Jerry. 1992. Advanced Organic Chemistry: reactions mechanisms and structure.  March Jerry. 1992. Advanced Organic Chemistry: reactions mechanisms and structure."},{"key":"e_1_3_2_2_26_1","volume-title":"Are All Spurious Features in Natural Language Alike? An Analysis through a Causal Lens. arXiv preprint arXiv:2210.14011","author":"Joshi Nitish","year":"2022","unstructured":"Nitish Joshi , Xiang Pan , and He He. 2022. Are All Spurious Features in Natural Language Alike? An Analysis through a Causal Lens. arXiv preprint arXiv:2210.14011 ( 2022 ). Nitish Joshi, Xiang Pan, and He He. 2022. Are All Spurious Features in Natural Language Alike? An Analysis through a Causal Lens. arXiv preprint arXiv:2210.14011 (2022)."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1021\/jacsau.1c00035"},{"key":"e_1_3_2_2_28_1","volume-title":"Experimental database of optical properties of organic compounds. Scientific data","author":"Joung Joonyoung F","year":"2020","unstructured":"Joonyoung F Joung , Minhi Han , Minseok Jeong , and Sungnam Park . 2020. Experimental database of optical properties of organic compounds. Scientific data , Vol. 7 , 1 ( 2020 ), 1--6. Joonyoung F Joung, Minhi Han, Minseok Jeong, and Sungnam Park. 2020. Experimental database of optical properties of organic compounds. Scientific data, Vol. 7, 1 (2020), 1--6."},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0196865"},{"key":"e_1_3_2_2_30_1","volume-title":"Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling . 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 ( 2016 ). Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)."},{"key":"e_1_3_2_2_31_1","volume-title":"Conditional Graph Information Bottleneck for Molecular Relational Learning. arXiv preprint arXiv:2305.01520","author":"Lee Namkyeong","year":"2023","unstructured":"Namkyeong Lee , Dongmin Hyun , Gyoung S Na , Sungwon Kim , Junseok Lee , and Chanyoung Park . 2023. Conditional Graph Information Bottleneck for Molecular Relational Learning. arXiv preprint arXiv:2305.01520 ( 2023 ). Namkyeong Lee, Dongmin Hyun, Gyoung S Na, Sungwon Kim, Junseok Lee, and Chanyoung Park. 2023. Conditional Graph Information Bottleneck for Molecular Relational Learning. arXiv preprint arXiv:2305.01520 (2023)."},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i7.20700"},{"key":"e_1_3_2_2_33_1","volume-title":"Ood-gnn: Out-of-distribution generalized graph neural network","author":"Li Haoyang","year":"2022","unstructured":"Haoyang Li , Xin Wang , Ziwei Zhang , and Wenwu Zhu . 2022 a. Ood-gnn: Out-of-distribution generalized graph neural network . IEEE Transactions on Knowledge and Data Engineering ( 2022). Haoyang Li, Xin Wang, Ziwei Zhang, and Wenwu Zhu. 2022a. Ood-gnn: Out-of-distribution generalized graph neural network. IEEE Transactions on Knowledge and Data Engineering (2022)."},{"key":"e_1_3_2_2_34_1","unstructured":"Haoyang Li Ziwei Zhang Xin Wang and Wenwu Zhu. 2022b. Learning invariant graph representations for out-of-distribution generalization. In Advances in Neural Information Processing Systems.  Haoyang Li Ziwei Zhang Xin Wang and Wenwu Zhu. 2022b. Learning invariant graph representations for out-of-distribution generalization. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_2_35_1","volume-title":"International conference on machine learning. PMLR, 3835--3845","author":"Li Yujia","year":"2019","unstructured":"Yujia Li , Chenjie Gu , Thomas Dullien , Oriol Vinyals , and Pushmeet Kohli . 2019 . Graph matching networks for learning the similarity of graph structured objects . In International conference on machine learning. PMLR, 3835--3845 . Yujia Li, Chenjie Gu, Thomas Dullien, Oriol Vinyals, and Pushmeet Kohli. 2019. Graph matching networks for learning the similarity of graph structured objects. In International conference on machine learning. PMLR, 3835--3845."},{"key":"e_1_3_2_2_36_1","volume-title":"Delfos: deep learning model for prediction of solvation free energies in generic organic solvents. Chemical science","author":"Lim Hyuntae","year":"2019","unstructured":"Hyuntae Lim and YounJoon Jung . 2019. Delfos: deep learning model for prediction of solvation free energies in generic organic solvents. Chemical science , Vol. 10 , 36 ( 2019 ), 8306--8315. Hyuntae Lim and YounJoon Jung. 2019. Delfos: deep learning model for prediction of solvation free energies in generic organic solvents. Chemical science, Vol. 10, 36 (2019), 8306--8315."},{"key":"e_1_3_2_2_37_1","unstructured":"Xiang Ling Lingfei Wu Saizhuo Wang Tengfei Ma Fangli Xu Chunming Wu and Shouling Ji. 2019. Hierarchical graph matching networks for deep graph similarity learning. (2019).  Xiang Ling Lingfei Wu Saizhuo Wang Tengfei Ma Fangli Xu Chunming Wu and Shouling Ji. 2019. Hierarchical graph matching networks for deep graph similarity learning. (2019)."},{"key":"e_1_3_2_2_38_1","volume-title":"DrugCombDB: a comprehensive database of drug combinations toward the discovery of combinatorial therapy. Nucleic acids research","author":"Liu Hui","year":"2020","unstructured":"Hui Liu , Wenhao Zhang , Bo Zou , Jinxian Wang , Yuanyuan Deng , and Lei Deng . 2020. DrugCombDB: a comprehensive database of drug combinations toward the discovery of combinatorial therapy. Nucleic acids research , Vol. 48 , D1 ( 2020 ), D871--D881. Hui Liu, Wenhao Zhang, Bo Zou, Jinxian Wang, Yuanyuan Deng, and Lei Deng. 2020. DrugCombDB: a comprehensive database of drug combinations toward the discovery of combinatorial therapy. Nucleic acids research, Vol. 48, D1 (2020), D871--D881."},{"key":"e_1_3_2_2_39_1","volume-title":"Parameterized explainer for graph neural network. Advances in neural information processing systems","author":"Luo Dongsheng","year":"2020","unstructured":"Dongsheng Luo , Wei Cheng , Dongkuan Xu , Wenchao Yu , Bo Zong , Haifeng Chen , and Xiang Zhang . 2020. Parameterized explainer for graph neural network. Advances in neural information processing systems , Vol. 33 ( 2020 ), 19620--19631. Dongsheng Luo, Wei Cheng, Dongkuan Xu, Wenchao Yu, Bo Zong, Haifeng Chen, and Xiang Zhang. 2020. Parameterized explainer for graph neural network. Advances in neural information processing systems, Vol. 33 (2020), 19620--19631."},{"key":"e_1_3_2_2_40_1","volume-title":"The concrete distribution: A continuous relaxation of discrete random variables. arXiv preprint arXiv:1611.00712","author":"Maddison Chris J","year":"2016","unstructured":"Chris J Maddison , Andriy Mnih , and Yee Whye Teh . 2016. The concrete distribution: A continuous relaxation of discrete random variables. arXiv preprint arXiv:1611.00712 ( 2016 ). Chris J Maddison, Andriy Mnih, and Yee Whye Teh. 2016. The concrete distribution: A continuous relaxation of discrete random variables. arXiv preprint arXiv:1611.00712 (2016)."},{"key":"e_1_3_2_2_41_1","volume-title":"Tom Claassen, Stephan Bongers, Philip Versteeg, and Joris M Mooij.","author":"Magliacane Sara","year":"2018","unstructured":"Sara Magliacane , Thijs Van Ommen , Tom Claassen, Stephan Bongers, Philip Versteeg, and Joris M Mooij. 2018 . Domain adaptation by using causal inference to predict invariant conditional distributions. Advances in neural information processing systems, Vol. 31 (2018). Sara Magliacane, Thijs Van Ommen, Tom Claassen, Stephan Bongers, Philip Versteeg, and Joris M Mooij. 2018. Domain adaptation by using causal inference to predict invariant conditional distributions. Advances in neural information processing systems, Vol. 31 (2018)."},{"key":"e_1_3_2_2_42_1","volume-title":"Minnesota solvation database (MNSOL) version","author":"Marenich Aleksandr V","year":"2012","unstructured":"Aleksandr V Marenich , Casey P Kelly , Jason D Thompson , Gregory D Hawkins , Candee C Chambers , David J Giesen , Paul Winget , Christopher J Cramer , and Donald G Truhlar . 2020. Minnesota solvation database (MNSOL) version 2012 . (2020). Aleksandr V Marenich, Casey P Kelly, Jason D Thompson, Gregory D Hawkins, Candee C Chambers, David J Giesen, Paul Winget, Christopher J Cramer, and Donald G Truhlar. 2020. Minnesota solvation database (MNSOL) version 2012. (2020)."},{"key":"e_1_3_2_2_43_1","unstructured":"Alan D McNaught Andrew Wilkinson etal 1997. Compendium of chemical terminology. Vol. 1669. Blackwell Science Oxford.  Alan D McNaught Andrew Wilkinson et al. 1997. Compendium of chemical terminology. Vol. 1669. Blackwell Science Oxford."},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10822-014-9747-x"},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1063\/1.5000910"},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bej.2021.108054"},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbab133"},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5433"},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.0c01413"},{"key":"e_1_3_2_2_51_1","volume-title":"Interpretation and identification of causal mediation. Psychological methods","author":"Pearl Judea","year":"2014","unstructured":"Judea Pearl . 2014. Interpretation and identification of causal mediation. Psychological methods , Vol. 19 , 4 ( 2014 ), 459. Judea Pearl. 2014. Interpretation and identification of causal mediation. Psychological methods, Vol. 19, 4 (2014), 459."},{"key":"e_1_3_2_2_52_1","volume-title":"Cambridge, UK","author":"Judea Pearl","year":"2000","unstructured":"Judea Pearl et al. 2000 . Models , reasoning and inference. Cambridge, UK : CambridgeUniversityPress , Vol . 19, 2 (2000). Judea Pearl et al. 2000. Models, reasoning and inference. Cambridge, UK: CambridgeUniversityPress, Vol. 19, 2 (2000)."},{"key":"e_1_3_2_2_53_1","unstructured":"Judea Pearl and Dana Mackenzie. 2018. The book of why: the new science of cause and effect. Basic books.  Judea Pearl and Dana Mackenzie. 2018. The book of why: the new science of cause and effect. Basic books."},{"key":"e_1_3_2_2_54_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btx806"},{"key":"e_1_3_2_2_55_1","doi-asserted-by":"publisher","DOI":"10.1039\/B610213C"},{"key":"e_1_3_2_2_56_1","doi-asserted-by":"publisher","DOI":"10.1002\/anie.196500291"},{"key":"e_1_3_2_2_57_1","volume-title":"A Unified View of Relational Deep Learning for Drug Pair Scoring. arXiv preprint arXiv:2111.02916","author":"Rozemberczki Benedek","year":"2021","unstructured":"Benedek Rozemberczki , Stephen Bonner , Andriy Nikolov , Michael Ughetto , Sebastian Nilsson , and Eliseo Papa . 2021. A Unified View of Relational Deep Learning for Drug Pair Scoring. arXiv preprint arXiv:2111.02916 ( 2021 ). Benedek Rozemberczki, Stephen Bonner, Andriy Nikolov, Michael Ughetto, Sebastian Nilsson, and Eliseo Papa. 2021. A Unified View of Relational Deep Learning for Drug Pair Scoring. arXiv preprint arXiv:2111.02916 (2021)."},{"key":"e_1_3_2_2_58_1","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557146"},{"key":"e_1_3_2_2_59_1","volume-title":"Anna Gogleva, Piotr Grabowski, Klas Karis, Andrej Lamov, Andriy Nikolov, Sebastian Nilsson, Michael Ughetto, Yu Wang, et al.","author":"Rozemberczki Benedek","year":"2022","unstructured":"Benedek Rozemberczki , Charles Tapley Hoyt , Anna Gogleva, Piotr Grabowski, Klas Karis, Andrej Lamov, Andriy Nikolov, Sebastian Nilsson, Michael Ughetto, Yu Wang, et al. 2022 b. ChemicalX: A Deep Learning Library for Drug Pair Scoring . arXiv preprint arXiv:2202.05240 (2022). Benedek Rozemberczki, Charles Tapley Hoyt, Anna Gogleva, Piotr Grabowski, Klas Karis, Andrej Lamov, Andriy Nikolov, Sebastian Nilsson, Michael Ughetto, Yu Wang, et al. 2022b. 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BMC medical research methodology","author":"Sidey-Gibbons Jenni AM","year":"2019","unstructured":"Jenni AM Sidey-Gibbons and Chris J Sidey-Gibbons . 2019. Machine learning in medicine: a practical introduction. BMC medical research methodology , Vol. 19 , 1 ( 2019 ), 1--18. Jenni AM Sidey-Gibbons and Chris J Sidey-Gibbons. 2019. Machine learning in medicine: a practical introduction. BMC medical research methodology, Vol. 19, 1 (2019), 1--18."},{"key":"e_1_3_2_2_64_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.saa.2009.03.004"},{"key":"e_1_3_2_2_65_1","volume-title":"March's advanced organic chemistry: reactions, mechanisms, and structure","author":"Smith Michael B","unstructured":"Michael B Smith . 2020. March's advanced organic chemistry: reactions, mechanisms, and structure . John Wiley & Sons . Michael B Smith. 2020. March's advanced organic chemistry: reactions, mechanisms, and structure. John Wiley & Sons."},{"key":"e_1_3_2_2_66_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539366"},{"key":"e_1_3_2_2_67_1","volume-title":"Mocl: Contrastive learning on molecular graphs with multi-level domain knowledge. arXiv preprint arXiv:2106.04509","author":"Sun Mengying","year":"2021","unstructured":"Mengying Sun , Jing Xing , Huijun Wang , Bin Chen , and Jiayu Zhou . 2021 . Mocl: Contrastive learning on molecular graphs with multi-level domain knowledge. arXiv preprint arXiv:2106.04509 (2021). Mengying Sun, Jing Xing, Huijun Wang, Bin Chen, and Jiayu Zhou. 2021. Mocl: Contrastive learning on molecular graphs with multi-level domain knowledge. arXiv preprint arXiv:2106.04509 (2021)."},{"key":"e_1_3_2_2_68_1","doi-asserted-by":"crossref","unstructured":"Jessica Vamathevan Dominic Clark Paul Czodrowski Ian Dunham Edgardo Ferran George Lee Bin Li Anant Madabhushi Parantu Shah Michaela Spitzer etal 2019. Applications of machine learning in drug discovery and development. 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Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)."},{"key":"e_1_3_2_2_70_1","first-page":"4","article-title":"Deep Graph Infomax","volume":"2","author":"Velickovic Petar","year":"2019","unstructured":"Petar Velickovic , William Fedus , William L Hamilton , Pietro Li\u00f2 , Yoshua Bengio , and R Devon Hjelm . 2019 . Deep Graph Infomax . ICLR (Poster) , Vol. 2 , 3 (2019), 4 . Petar Velickovic, William Fedus, William L Hamilton, Pietro Li\u00f2, Yoshua Bengio, and R Devon Hjelm. 2019. Deep Graph Infomax. ICLR (Poster), Vol. 2, 3 (2019), 4.","journal-title":"ICLR (Poster)"},{"key":"e_1_3_2_2_71_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cej.2021.129307"},{"key":"e_1_3_2_2_72_1","doi-asserted-by":"publisher","DOI":"10.1136\/amiajnl-2012-000935"},{"key":"e_1_3_2_2_73_1","volume-title":"Order matters: Sequence to sequence for sets. arXiv preprint arXiv:1511.06391","author":"Vinyals Oriol","year":"2015","unstructured":"Oriol Vinyals , Samy Bengio , and Manjunath Kudlur . 2015. Order matters: Sequence to sequence for sets. arXiv preprint arXiv:1511.06391 ( 2015 ). Oriol Vinyals, Samy Bengio, and Manjunath Kudlur. 2015. Order matters: Sequence to sequence for sets. arXiv preprint arXiv:1511.06391 (2015)."},{"key":"e_1_3_2_2_74_1","volume-title":"Chemical-reaction-aware molecule representation learning. arXiv preprint arXiv:2109.09888","author":"Wang Hongwei","year":"2021","unstructured":"Hongwei Wang , Weijiang Li , Xiaomeng Jin , Kyunghyun Cho , Heng Ji , Jiawei Han , and Martin D Burke . 2021a. Chemical-reaction-aware molecule representation learning. arXiv preprint arXiv:2109.09888 ( 2021 ). Hongwei Wang, Weijiang Li, Xiaomeng Jin, Kyunghyun Cho, Heng Ji, Jiawei Han, and Martin D Burke. 2021a. Chemical-reaction-aware molecule representation learning. arXiv preprint arXiv:2109.09888 (2021)."},{"key":"e_1_3_2_2_75_1","volume-title":"Identifying and mitigating spurious correlations for improving robustness in nlp models. arXiv preprint arXiv:2110.07736","author":"Wang Tianlu","year":"2021","unstructured":"Tianlu Wang , Diyi Yang , and Xuezhi Wang . 2021c. Identifying and mitigating spurious correlations for improving robustness in nlp models. arXiv preprint arXiv:2110.07736 ( 2021 ). Tianlu Wang, Diyi Yang, and Xuezhi Wang. 2021c. Identifying and mitigating spurious correlations for improving robustness in nlp models. arXiv preprint arXiv:2110.07736 (2021)."},{"key":"e_1_3_2_2_76_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2012.28"},{"key":"e_1_3_2_2_77_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449786"},{"key":"e_1_3_2_2_78_1","doi-asserted-by":"publisher","DOI":"10.1021\/ci00057a005"},{"key":"e_1_3_2_2_79_1","doi-asserted-by":"crossref","unstructured":"David S Wishart Yannick D Feunang An C Guo Elvis J Lo Ana Marcu Jason R Grant Tanvir Sajed Daniel Johnson Carin Li Zinat Sayeeda etal 2018. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic acids research Vol. 46 D1 (2018) D1074--D1082.  David S Wishart Yannick D Feunang An C Guo Elvis J Lo Ana Marcu Jason R Grant Tanvir Sajed Daniel Johnson Carin Li Zinat Sayeeda et al. 2018. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic acids research Vol. 46 D1 (2018) D1074--D1082.","DOI":"10.1093\/nar\/gkx1037"},{"key":"e_1_3_2_2_80_1","volume-title":"Discovering invariant rationales for graph neural networks. arXiv preprint arXiv:2201.12872","author":"Wu Ying-Xin","year":"2022","unstructured":"Ying-Xin Wu , Xiang Wang , An Zhang , Xiangnan He , and Tat-Seng Chua . 2022. Discovering invariant rationales for graph neural networks. arXiv preprint arXiv:2201.12872 ( 2022 ). Ying-Xin Wu, Xiang Wang, An Zhang, Xiangnan He, and Tat-Seng Chua. 2022. Discovering invariant rationales for graph neural networks. arXiv preprint arXiv:2201.12872 (2022)."},{"key":"e_1_3_2_2_81_1","volume-title":"How powerful are graph neural networks? arXiv preprint arXiv:1810.00826","author":"Xu Keyulu","year":"2018","unstructured":"Keyulu Xu , Weihua Hu , Jure Leskovec , and Stefanie Jegelka . 2018. How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 ( 2018 ). Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2018. How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018)."},{"key":"e_1_3_2_2_82_1","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3134018"},{"key":"e_1_3_2_2_83_1","volume-title":"Gnnexplainer: Generating explanations for graph neural networks. Advances in neural information processing systems","author":"Ying Zhitao","year":"2019","unstructured":"Zhitao Ying , Dylan Bourgeois , Jiaxuan You , Marinka Zitnik , and Jure Leskovec . 2019 . Gnnexplainer: Generating explanations for graph neural networks. Advances in neural information processing systems , Vol. 32 (2019). Zhitao Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec. 2019. Gnnexplainer: Generating explanations for graph neural networks. Advances in neural information processing systems, Vol. 32 (2019)."},{"key":"e_1_3_2_2_84_1","volume-title":"International Conference on Machine Learning. PMLR, 12241--12252","author":"Yuan Hao","year":"2021","unstructured":"Hao Yuan , Haiyang Yu , Jie Wang , Kang Li , and Shuiwang Ji . 2021 . On explainability of graph neural networks via subgraph explorations . In International Conference on Machine Learning. PMLR, 12241--12252 . Hao Yuan, Haiyang Yu, Jie Wang, Kang Li, and Shuiwang Ji. 2021. On explainability of graph neural networks via subgraph explorations. In International Conference on Machine Learning. PMLR, 12241--12252."},{"key":"e_1_3_2_2_85_1","volume-title":"Interventional few-shot learning. Advances in neural information processing systems","author":"Yue Zhongqi","year":"2020","unstructured":"Zhongqi Yue , Hanwang Zhang , Qianru Sun , and Xian-Sheng Hua . 2020. Interventional few-shot learning. Advances in neural information processing systems , Vol. 33 ( 2020 ), 2734--2746. Zhongqi Yue, Hanwang Zhang, Qianru Sun, and Xian-Sheng Hua. 2020. Interventional few-shot learning. Advances in neural information processing systems, Vol. 33 (2020), 2734--2746."},{"key":"e_1_3_2_2_86_1","first-page":"655","article-title":"Causal intervention for weakly-supervised semantic segmentation","volume":"33","author":"Zhang Dong","year":"2020","unstructured":"Dong Zhang , Hanwang Zhang , Jinhui Tang , Xian-Sheng Hua , and Qianru Sun . 2020 . Causal intervention for weakly-supervised semantic segmentation . Advances in Neural Information Processing Systems , Vol. 33 (2020), 655 -- 666 . Dong Zhang, Hanwang Zhang, Jinhui Tang, Xian-Sheng Hua, and Qianru Sun. 2020. Causal intervention for weakly-supervised semantic segmentation. Advances in Neural Information Processing Systems, Vol. 33 (2020), 655--666.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_87_1","volume-title":"Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data. BMC bioinformatics","author":"Zhang Wen","year":"2017","unstructured":"Wen Zhang , Yanlin Chen , Feng Liu , Fei Luo , Gang Tian , and Xiaohong Li. 2017. Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data. BMC bioinformatics , Vol. 18 , 1 ( 2017 ), 1--12. Wen Zhang, Yanlin Chen, Feng Liu, Fei Luo, Gang Tian, and Xiaohong Li. 2017. Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data. 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