{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T15:27:46Z","timestamp":1780586866211,"version":"3.54.1"},"publisher-location":"New York, NY, USA","reference-count":37,"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":[{"name":"NSFC General Program","award":["62176215"],"award-info":[{"award-number":["62176215"]}]},{"name":"Science and Technology Innovation 2030-Major Project","award":["2022ZD0208800"],"award-info":[{"award-number":["2022ZD0208800"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,8,14]]},"DOI":"10.1145\/3534678.3539457","type":"proceedings-article","created":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T19:06:41Z","timestamp":1660331201000},"page":"1635-1645","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":30,"title":["Learning on Graphs with Out-of-Distribution Nodes"],"prefix":"10.1145","author":[{"given":"Yu","family":"Song","sequence":"first","affiliation":[{"name":"Westlake University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Donglin","family":"Wang","sequence":"additional","affiliation":[{"name":"Westlake University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,8,14]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Novelty detection using ensembles with regularized disagreement. arXiv preprint arXiv:1811.05868","author":"Eric Stavarache","year":"2021","unstructured":"Stavarache Eric, and Yang Fanny. 2021. Novelty detection using ensembles with regularized disagreement. arXiv preprint arXiv:1811.05868 (2021)."},{"key":"e_1_3_2_1_2_1","volume-title":"International Conference on Machine Learning. PMLR, 837--851","author":"Bevilacqua Beatrice","year":"2021","unstructured":"Beatrice Bevilacqua, Yangze Zhou, and Bruno Ribeiro. 2021. Size-invariant graph representations for graph classification extrapolations. In International Conference on Machine Learning. PMLR, 837--851."},{"key":"e_1_3_2_1_3_1","volume-title":"How Attentive are Graph Attention Networks? arXiv preprint arXiv:2105.14491","author":"Brody Shaked","year":"2021","unstructured":"Shaked Brody, Uri Alon, and Eran Yahav. 2021. How Attentive are Graph Attention Networks? arXiv preprint arXiv:2105.14491 (2021)."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467364"},{"key":"e_1_3_2_1_5_1","first-page":"281","article-title":"Semi-supervised learning by entropy minimization","volume":"367","author":"Grandvalet Yves","year":"2005","unstructured":"Yves Grandvalet, Yoshua Bengio, et al. 2005. Semi-supervised learning by entropy minimization. CAP 367 (2005), 281--296.","journal-title":"CAP"},{"key":"e_1_3_2_1_6_1","volume-title":"International Conference on Machine Learning. PMLR, 3897--3906","author":"Guo Lan-Zhe","year":"2020","unstructured":"Lan-Zhe Guo, Zhen-Yu Zhang, Yuan Jiang, Yu-Feng Li, and Zhi-Hua Zhou. 2020. Safe deep semi-supervised learning for unseen-class unlabeled data. In International Conference on Machine Learning. PMLR, 3897--3906."},{"key":"e_1_3_2_1_7_1","volume-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems. 1025--1035","author":"Hamilton William L","year":"2017","unstructured":"William L Hamilton, Rex Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Proceedings of the 31st International Conference on Neural Information Processing Systems. 1025--1035."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00013"},{"key":"e_1_3_2_1_9_1","volume-title":"International Conference on Learning Representations.","author":"Hendrycks Dan","year":"2017","unstructured":"Dan Hendrycks and Kevin Gimpel. 2017. A baseline for detecting misclassified and out-of-distribution examples in neural networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_10_1","volume-title":"International Conference on Learning Representations.","author":"Hendrycks Dan","year":"2019","unstructured":"Dan Hendrycks, Mantas Mazeika, and Thomas Dietterich. 2019. Deep anomaly detection with outlier exposure. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_11_1","volume-title":"International Conference on Learning Representations.","author":"Kipf Thomas N","year":"2017","unstructured":"Thomas N Kipf and MaxWelling. 2017. Semi-supervised classification with graph convolutional networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_12_1","unstructured":"Saito Kuniaki Kim Donghyun and Saenko Kate. 2021. OpenMatch: Open-set Consistency Regularization for Semi-supervised Learning with Outliers. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_1_13_1","volume-title":"Training confidencecalibrated classifiers for detecting out-of-distribution samples. arXiv preprint arXiv:1711.09325","author":"Lee Kimin","year":"2017","unstructured":"Kimin Lee, Honglak Lee, Kibok Lee, and Jinwoo Shin. 2017. Training confidencecalibrated classifiers for detecting out-of-distribution samples. arXiv preprint arXiv:1711.09325 (2017)."},{"key":"e_1_3_2_1_14_1","unstructured":"Kimin Lee Kibok Lee Honglak Lee and Jinwoo Shin. 2018. A simple unified framework for detecting out-of-distribution samples and adversarial attacks. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"e_1_3_2_1_16_1","volume-title":"International Conference on Learning Representations.","author":"Liang Shiyu","year":"2018","unstructured":"Shiyu Liang, Yixuan Li, and R Srikant. 2018. Enhancing the reliability of out-ofdistribution image detection in neural networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN52387.2021.9533899"},{"key":"e_1_3_2_1_18_1","volume-title":"Vincent Auvray, and Anuj Goyal.","author":"Marek Petr","year":"2021","unstructured":"Petr Marek, Vishal Ishwar Naik, Vincent Auvray, and Anuj Goyal. 2021. OodGAN: Generative Adversarial Network for Out-of-Domain Data Generation. arXiv preprint arXiv:2104.02484 (2021)."},{"key":"e_1_3_2_1_19_1","volume-title":"Wiki-cs: A wikipedia-based benchmark for graph neural networks. arXiv preprint arXiv:2007.02901","author":"Mernyei P\u00e9ter","year":"2020","unstructured":"P\u00e9ter Mernyei and C\u0103t\u0103lina Cangea. 2020. Wiki-cs: A wikipedia-based benchmark for graph neural networks. arXiv preprint arXiv:2007.02901 (2020)."},{"key":"e_1_3_2_1_20_1","volume-title":"Virtual adversarial training: a regularization method for supervised and semi-supervised learning","author":"Miyato Takeru","year":"2018","unstructured":"Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, and Shin Ishii. 2018. Virtual adversarial training: a regularization method for supervised and semi-supervised learning. IEEE transactions on pattern analysis and machine intelligence 41, 8 (2018), 1979--1993."},{"key":"e_1_3_2_1_21_1","volume-title":"Ekin Dogus Cubuk, and Ian Goodfellow","author":"Oliver Avital","year":"2018","unstructured":"Avital Oliver, Augustus Odena, Colin A Raffel, Ekin Dogus Cubuk, and Ian Goodfellow. 2018. Realistic evaluation of deep semi-supervised learning algorithms. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_1_22_1","volume-title":"International Conference on Learning Representations.","author":"Pei Hongbin","year":"2020","unstructured":"Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, and Bo Yang. 2020. Geom-gcn: Geometric graph convolutional networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_23_1","unstructured":"Jie Ren Peter J Liu Emily Fertig Jasper Snoek Ryan Poplin Mark Depristo Joshua Dillon and Balaji Lakshminarayanan. 2019. Likelihood Ratios for Out-of- Distribution Detection. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411866"},{"key":"e_1_3_2_1_25_1","volume-title":"International Conference on Learning Representations.","author":"Sehwag Vikash","year":"2021","unstructured":"Vikash Sehwag, Mung Chiang, and Prateek Mittal. 2021. Ssd: A unified framework for self-supervised outlier detection. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_26_1","volume-title":"Pitfalls of graph neural network evaluation. arXiv preprint arXiv:1811.05868","author":"Shchur Oleksandr","year":"2018","unstructured":"Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, and Stephan G\u00fcnnemann. 2018. Pitfalls of graph neural network evaluation. arXiv preprint arXiv:1811.05868 (2018)."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.6048"},{"key":"e_1_3_2_1_28_1","volume-title":"Csi: Novelty detection via contrastive learning on distributionally shifted instances. In Advances in neural information processing systems.","author":"Tack Jihoon","year":"2020","unstructured":"Jihoon Tack, Sangwoo Mo, Jongheon Jeong, and Jinwoo Shin. 2020. Csi: Novelty detection via contrastive learning on distributionally shifted instances. In Advances in neural information processing systems."},{"key":"e_1_3_2_1_29_1","volume-title":"Graph attention networks. arXiv preprint arXiv:1710.10903","author":"Veli\u010dkovi\u0107 Petar","year":"2017","unstructured":"Petar Veli\u010dkovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01237-3_34"},{"key":"e_1_3_2_1_31_1","unstructured":"Xiao Wang Hongrui Liu Chuan Shi and Cheng Yang. 2021. Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_1_32_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 semisupervised learning with graph embeddings. In International conference on machine learning. PMLR, 40--48."},{"key":"e_1_3_2_1_33_1","volume-title":"International Conference on Machine Learning. PMLR, 11975--11986","author":"Yehudai Gilad","year":"2021","unstructured":"Gilad Yehudai, Ethan Fetaya, Eli Meirom, Gal Chechik, and Haggai Maron. 2021. From local structures to size generalization in graph neural networks. In International Conference on Machine Learning. PMLR, 11975--11986."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00961"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58610-2_26"},{"key":"e_1_3_2_1_36_1","unstructured":"Xujiang Zhao Feng Chen Shu Hu and Jin-Hee Cho. 2020. Uncertainty Aware Semi-Supervised Learning on Graph Data. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_1_37_1","volume-title":"STEP: Out-of-Distribution Detection in the Presence of Limited In-distribution Labeled Data. In Advances in Neural Information Processing Systems.","author":"Zhou Zhi","year":"2021","unstructured":"Zhi Zhou, Lan-Zhe Guo, Zhanzhan Cheng, Yu-Feng Li, and Shiliang Pu. 2021. STEP: Out-of-Distribution Detection in the Presence of Limited In-distribution Labeled Data. In Advances in Neural Information Processing Systems."}],"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\/3534678.3539457","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3534678.3539457","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:03:03Z","timestamp":1750186983000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3539457"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,14]]},"references-count":37,"alternative-id":["10.1145\/3534678.3539457","10.1145\/3534678"],"URL":"https:\/\/doi.org\/10.1145\/3534678.3539457","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"}}]}}