{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T21:14:21Z","timestamp":1772918061842,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":49,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,8,14]],"date-time":"2022-08-14T00:00:00Z","timestamp":1660435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["1849816, 2142827, 2146761, 2102592"],"award-info":[{"award-number":["1849816, 2142827, 2146761, 2102592"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,8,14]]},"DOI":"10.1145\/3534678.3539347","type":"proceedings-article","created":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T19:06:41Z","timestamp":1660331201000},"page":"1069-1078","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":60,"title":["Graph Rationalization with Environment-based Augmentations"],"prefix":"10.1145","author":[{"given":"Gang","family":"Liu","sequence":"first","affiliation":[{"name":"University of Notre Dame, Notre Dame, IN, USA"}]},{"given":"Tong","family":"Zhao","sequence":"additional","affiliation":[{"name":"University of Notre Dame, Notre Dame, IN, USA"}]},{"given":"Jiaxin","family":"Xu","sequence":"additional","affiliation":[{"name":"University of Notre Dame, Notre Dame, IN, USA"}]},{"given":"Tengfei","family":"Luo","sequence":"additional","affiliation":[{"name":"University of Notre Dame, Notre Dame, IN, USA"}]},{"given":"Meng","family":"Jiang","sequence":"additional","affiliation":[{"name":"University of Notre Dame, Notre Dame, IN, USA"}]}],"member":"320","published-online":{"date-parts":[[2022,8,14]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Martin Arjovsky L\u00e9on Bottou Ishaan Gulrajani and David Lopez-Paz. 2019. Invariant risk minimization. In arXiv:1907.02893 ."},{"key":"e_1_3_2_1_2_1","unstructured":"Shiyu Chang Yang Zhang Mo Yu and Tommi Jaakkola. 2020. Invariant rationalization. In ICML. 1448--1458."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5747"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.mser.2020.100595"},{"key":"e_1_3_2_1_5_1","unstructured":"Shaohua Fan Xiao Wang Chuan Shi Peng Cui and Bai Wang. 2021. Generalizing Graph Neural Networks on Out-Of-Distribution Graphs. In arXiv:2111.10657 ."},{"key":"e_1_3_2_1_6_1","unstructured":"Hongyang Gao and Shuiwang Ji. 2021. Graph U-Nets. IEEE TPAMI (2021)."},{"key":"e_1_3_2_1_7_1","unstructured":"Zhichun Guo Chuxu Zhang Wenhao Yu John Herr Olaf Wiest Meng Jiang and Nitesh V Chawla. 2021. Few-Shot Graph Learning for Molecular Property Prediction. In WWW. 2559--2567."},{"key":"e_1_3_2_1_8_1","unstructured":"William L Hamilton Rex Ying and Jure Leskovec. 2017. Inductive representation learning on large graphs. In NeurIPS. 1025--1035."},{"key":"e_1_3_2_1_9_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 ."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3501808","article-title":"Federated Dynamic Graph Neural Networks with Secure Aggregation for Video-based Distributed Surveillance","volume":"13","author":"Jiang Meng","year":"2022","unstructured":"Meng Jiang, Taeho Jung, Ryan Karl, and Tong Zhao. 2022. Federated Dynamic Graph Neural Networks with Secure Aggregation for Video-based Distributed Surveillance. TIST , Vol. 13, 4 (2022), 1--23.","journal-title":"TIST"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jpcc.8b02913"},{"key":"e_1_3_2_1_12_1","unstructured":"Thomas N Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In ICLR ."},{"key":"e_1_3_2_1_13_1","unstructured":"Greg Landrum. 2013. RDKit: A software suite for cheminformatics computational chemistry and predictive modeling."},{"key":"e_1_3_2_1_14_1","unstructured":"Junhyun Lee Inyeop Lee and Jaewoo Kang. 2019. Self-attention graph pooling. In ICML. 3734--3743."},{"key":"e_1_3_2_1_15_1","unstructured":"Haoyang Li Xin Wang Ziwei Zhang and Wenwu Zhu. 2021. OOD-GNN: Out-of-Distribution Generalized Graph Neural Network. In arXiv:2112.03806 ."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.9b00358"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.0c00726"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"crossref","unstructured":"Yao Ma Xiaorui Liu Tong Zhao Yozen Liu Jiliang Tang and Neil Shah. 2021. A unified view on graph neural networks as graph signal denoising. In CIKM . 1202--1211.","DOI":"10.1145\/3459637.3482225"},{"key":"e_1_3_2_1_19_1","unstructured":"Diego Mesquita Amauri Souza and Samuel Kaski. 2020. Rethinking pooling in graph neural networks. In NeurIPS ."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/EIDWT.2011.13"},{"key":"e_1_3_2_1_21_1","unstructured":"Hyeonjin Park Seunghun Lee Sihyeon Kim Jinyoung Park Jisu Jeong Kyung-Min Kim Jung-Woo Ha and Hyunwoo J Kim. 2021. Metropolis-Hastings Data Augmentation for Graph Neural Networks. In NeurIPS ."},{"key":"e_1_3_2_1_22_1","unstructured":"Yu Rong Wenbing Huang Tingyang Xu and Junzhou Huang. 2019. DropEdge: Towards Deep Graph Convolutional Networks on Node Classification. In ICLR ."},{"key":"e_1_3_2_1_23_1","volume-title":"Pradeep Kumar Ravikumar, and Andrej Risteski","author":"Rosenfeld Elan","year":"2021","unstructured":"Elan Rosenfeld, Pradeep Kumar Ravikumar, and Andrej Risteski. 2021. The Risks of Invariant Risk Minimization. In ICLR ."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.polymer.2013.05.075"},{"key":"e_1_3_2_1_25_1","unstructured":"A Thornton L Robeson B Freeman and D Uhlmann. 2012. Polymer Gas Separation Membrane Database."},{"key":"e_1_3_2_1_26_1","unstructured":"Petar Velivc kovi\u0107 Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Li\u00f2 and Yoshua Bengio. 2018. Graph Attention Networks. In ICLR ."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"crossref","unstructured":"Daheng Wang Meng Jiang Munira Syed Oliver Conway Vishal Juneja Sriram Subramanian and Nitesh V Chawla. 2020 a. Calendar graph neural networks for modeling time structures in spatiotemporal user behaviors. In KDD. 2581--2589.","DOI":"10.1145\/3394486.3403308"},{"key":"e_1_3_2_1_28_1","volume-title":"2021 a. Modeling co-evolution of attributed and structural information in graph sequence","author":"Wang Daheng","year":"2021","unstructured":"Daheng Wang, Zhihan Zhang, Yihong Ma, Tong Zhao, Tianwen Jiang, Nitesh Chawla, and Meng Jiang. 2021 a. Modeling co-evolution of attributed and structural information in graph sequence. IEEE TKDE (2021)."},{"key":"e_1_3_2_1_29_1","volume-title":"2021 b. Modeling co-evolution of attributed and structural information in graph sequence","author":"Wang Daheng","year":"2021","unstructured":"Daheng Wang, Zhihan Zhang, Yihong Ma, Tong Zhao, Tianwen Jiang, Nitesh Chawla, and Meng Jiang. 2021 b. Modeling co-evolution of attributed and structural information in graph sequence. IEEE TKDE (2021)."},{"key":"e_1_3_2_1_30_1","volume-title":"2021 c. Dynamic Attributed Graph Prediction with Conditional Normalizing Flows","author":"Wang Daheng","unstructured":"Daheng Wang, Tong Zhao, Nitesh V Chawla, and Meng Jiang. 2021 c. Dynamic Attributed Graph Prediction with Conditional Normalizing Flows. In ICDM. IEEE, 1385--1390."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"crossref","unstructured":"Yiwei Wang Wei Wang Yuxuan Liang Yujun Cai and Bryan Hooi. 2020 b. Graphcrop: Subgraph cropping for graph classification. In arXiv:2009.10564 .","DOI":"10.1145\/3442381.3450025"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"crossref","unstructured":"Yiwei Wang Wei Wang Yuxuan Liang Yujun Cai Juncheng Liu and Bryan Hooi. 2020 c. Nodeaug: Semi-supervised node classification with data augmentation. In KDD . 207--217.","DOI":"10.1145\/3394486.3403063"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1115\/1.4050557"},{"key":"e_1_3_2_1_34_1","unstructured":"Yingxin 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_1_35_1","first-page":"4","article-title":"A comprehensive survey on graph neural networks","volume":"32","author":"Wu Zonghan","year":"2020","unstructured":"Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip. 2020. A comprehensive survey on graph neural networks. IEEE TNNLS , Vol. 32, 1 (2020), 4--24.","journal-title":"IEEE TNNLS"},{"key":"e_1_3_2_1_36_1","volume-title":"MoleculeNet: a benchmark for molecular machine learning. Chemical science","author":"Wu Zhenqin","year":"2018","unstructured":"Zhenqin Wu, Bharath Ramsundar, Evan N Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S Pappu, Karl Leswing, and Vijay Pande. 2018. MoleculeNet: a benchmark for molecular machine learning. Chemical science (2018), 513--530."},{"key":"e_1_3_2_1_37_1","unstructured":"Keyulu Xu Weihua Hu Jure Leskovec and Stefanie Jegelka. 2019. How Powerful are Graph Neural Networks?. In ICLR ."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"crossref","unstructured":"Jason Yang Lei Tao Jinlong He Jeffrey McCutcheon and Ying Li. 2021. Discovery of Innovative Polymers for Next-Generation Gas-Separation Membranes using Interpretable Machine Learning. In chemrxiv-2021-p4g7z .","DOI":"10.26434\/chemrxiv-2021-p4g7z"},{"key":"e_1_3_2_1_39_1","volume-title":"Gnnexplainer: Generating explanations for graph neural networks. In NeurIPS .","author":"Ying Rex","year":"2019","unstructured":"Rex Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec. 2019. Gnnexplainer: Generating explanations for graph neural networks. In NeurIPS ."},{"key":"e_1_3_2_1_40_1","unstructured":"Rex Ying Jiaxuan You Christopher Morris Xiang Ren William L Hamilton and Jure Leskovec. 2018. Hierarchical graph representation learning with differentiable pooling. In NeurIPS . 4805--4815."},{"key":"e_1_3_2_1_41_1","unstructured":"Yuning You Tianlong Chen Yongduo Sui Ting Chen Zhangyang Wang and Yang Shen. 2020. Graph contrastive learning with augmentations. In NeurIPS. 5812--5823."},{"key":"e_1_3_2_1_42_1","volume-title":"Deep learning on graphs: A survey","author":"Zhang Ziwei","year":"2020","unstructured":"Ziwei Zhang, Peng Cui, and Wenwu Zhu. 2020. Deep learning on graphs: A survey. IEEE TKDE (2020)."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3102609"},{"key":"e_1_3_2_1_44_1","volume-title":"Graph Data Augmentation for Graph Machine Learning: A Survey. arXiv preprint arXiv:2202.08871","author":"Zhao Tong","year":"2022","unstructured":"Tong Zhao, Gang Liu, Stephan G\u00fcnnemann, and Meng Jiang. 2022a. Graph Data Augmentation for Graph Machine Learning: A Survey. arXiv preprint arXiv:2202.08871 (2022)."},{"key":"e_1_3_2_1_45_1","volume-title":"Learning from Counterfactual Links for Link Prediction. ICML","author":"Zhao Tong","year":"2022","unstructured":"Tong Zhao, Gang Liu, Daheng Wang, Wenhao Yu, and Meng Jiang. 2022b. Learning from Counterfactual Links for Link Prediction. ICML (2022)."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"crossref","unstructured":"Tong Zhao Yozen Liu Leonardo Neves Oliver Woodford Meng Jiang and Neil Shah. 2021b. Data Augmentation for Graph Neural Networks. In AAAI. 11015.","DOI":"10.1609\/aaai.v35i12.17315"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"crossref","unstructured":"Tong Zhao Bo Ni Wenhao Yu Zhichun Guo Neil Shah and Meng Jiang. 2021c. Action Sequence Augmentation for Early Graph-based Anomaly Detection. In CIKM . 2668--2678.","DOI":"10.1145\/3459637.3482313"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"crossref","unstructured":"Jiajun Zhou Jie Shen and Qi Xuan. 2020. Data Augmentation for Graph Classification. In CIKM. 2341--2344.","DOI":"10.1145\/3340531.3412086"},{"key":"e_1_3_2_1_49_1","unstructured":"Yanqiao Zhu Yichen Xu Feng Yu Qiang Liu Shu Wu and Liang Wang. 2021. Graph contrastive learning with adaptive augmentation. In WWW . 2069--2080."}],"event":{"name":"KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Washington DC USA","acronym":"KDD '22","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3539347","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3534678.3539347","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3534678.3539347","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:02:47Z","timestamp":1750186967000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3539347"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,14]]},"references-count":49,"alternative-id":["10.1145\/3534678.3539347","10.1145\/3534678"],"URL":"https:\/\/doi.org\/10.1145\/3534678.3539347","relation":{},"subject":[],"published":{"date-parts":[[2022,8,14]]},"assertion":[{"value":"2022-08-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}