{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T10:03:01Z","timestamp":1775815381864,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":60,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T00:00:00Z","timestamp":1691107200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62192784, U1936104, U20B2045, 62172052, 62002029"],"award-info":[{"award-number":["62192784, U1936104, U20B2045, 62172052, 62002029"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,8,6]]},"DOI":"10.1145\/3580305.3599244","type":"proceedings-article","created":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T18:10:58Z","timestamp":1691172658000},"page":"638-648","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":21,"title":["A Data-centric Framework to Endow Graph Neural Networks with Out-Of-Distribution Detection Ability"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1913-014X","authenticated-orcid":false,"given":"Yuxin","family":"Guo","sequence":"first","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7821-0030","authenticated-orcid":false,"given":"Cheng","family":"Yang","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3636-1508","authenticated-orcid":false,"given":"Yuluo","family":"Chen","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9924-0148","authenticated-orcid":false,"given":"Jixi","family":"Liu","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3734-0266","authenticated-orcid":false,"given":"Chuan","family":"Shi","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9402-3806","authenticated-orcid":false,"given":"Junping","family":"Du","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2023,8,4]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Concrete problems in AI safety. arXiv preprint arXiv:1606.06565","author":"Amodei Dario","year":"2016","unstructured":"Dario Amodei , Chris Olah , Jacob Steinhardt , Paul Christiano , John Schulman , and Dan Man\u00e9 . 2016. Concrete problems in AI safety. arXiv preprint arXiv:1606.06565 ( 2016 ). Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Man\u00e9. 2016. Concrete problems in AI safety. arXiv preprint arXiv:1606.06565 (2016)."},{"key":"e_1_3_2_2_2_1","volume-title":"Marin Or\u0161i\u0107, and Sinivs a \u0160egvi\u0107.","author":"Bevandi\u0107 Petra","year":"2018","unstructured":"Petra Bevandi\u0107 , Ivan Krevs o , Marin Or\u0161i\u0107, and Sinivs a \u0160egvi\u0107. 2018 . Discriminative out-of-distribution detection for semantic segmentation. arXiv preprint arXiv:1808.07703 (2018). Petra Bevandi\u0107, Ivan Krevs o, Marin Or\u0161i\u0107, and Sinivs a \u0160egvi\u0107. 2018. Discriminative out-of-distribution detection for semantic segmentation. arXiv preprint arXiv:1808.07703 (2018)."},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bti1007"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"crossref","unstructured":"Markus M Breunig Hans-Peter Kriegel Raymond T Ng and J\u00f6rg Sander. 2000. LOF: identifying density-based local outliers. In SIGMOD. 93--104.  Markus M Breunig Hans-Peter Kriegel Raymond T Ng and J\u00f6rg Sander. 2000. LOF: identifying density-based local outliers. In SIGMOD. 93--104.","DOI":"10.1145\/335191.335388"},{"key":"e_1_3_2_2_5_1","unstructured":"Ming Chen Zhewei Wei Zengfeng Huang Bolin Ding and Yaliang Li. 2020b. Simple and deep graph convolutional networks. In ICML. PMLR 1725--1735.  Ming Chen Zhewei Wei Zengfeng Huang Bolin Ding and Yaliang Li. 2020b. Simple and deep graph convolutional networks. In ICML. PMLR 1725--1735."},{"key":"e_1_3_2_2_6_1","volume-title":"A boundary based out-of-distribution classifier for generalized zero-shot learning","author":"Chen Xingyu","unstructured":"Xingyu Chen , Xuguang Lan , Fuchun Sun , and Nanning Zheng . 2020a. A boundary based out-of-distribution classifier for generalized zero-shot learning . In ECCV. Springer , 572--588. Xingyu Chen, Xuguang Lan, Fuchun Sun, and Nanning Zheng. 2020a. A boundary based out-of-distribution classifier for generalized zero-shot learning. In ECCV. Springer, 572--588."},{"key":"e_1_3_2_2_7_1","volume-title":"Dark model adaptation: Semantic image segmentation from daytime to nighttime","author":"Dai Dengxin","unstructured":"Dengxin Dai and Luc Van Gool . 2018. Dark model adaptation: Semantic image segmentation from daytime to nighttime . In ITSC. IEEE , 3819--3824. Dengxin Dai and Luc Van Gool. 2018. Dark model adaptation: Semantic image segmentation from daytime to nighttime. In ITSC. IEEE, 3819--3824."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"crossref","unstructured":"Jesse Davis and Mark Goadrich. 2006. The relationship between Precision-Recall and ROC curves. In ICML. 233--240.  Jesse Davis and Mark Goadrich. 2006. The relationship between Precision-Recall and ROC curves. In ICML. 233--240.","DOI":"10.1145\/1143844.1143874"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"crossref","unstructured":"Kaize Ding Jundong Li Nitin Agarwal and Huan Liu. 2021. Inductive anomaly detection on attributed networks. In IJCAI. 1288--1294.  Kaize Ding Jundong Li Nitin Agarwal and Huan Liu. 2021. Inductive anomaly detection on attributed networks. In IJCAI. 1288--1294.","DOI":"10.24963\/ijcai.2020\/179"},{"key":"e_1_3_2_2_10_1","volume-title":"Deep anomaly detection on attributed networks","author":"Ding Kaize","unstructured":"Kaize Ding , Jundong Li , Rohit Bhanushali , and Huan Liu . 2019. Deep anomaly detection on attributed networks . In ICDM. SIAM , 594--602. Kaize Ding, Jundong Li, Rohit Bhanushali, and Huan Liu. 2019. Deep anomaly detection on attributed networks. In ICDM. SIAM, 594--602."},{"key":"e_1_3_2_2_11_1","unstructured":"Justin Gilmer Samuel S Schoenholz Patrick F Riley Oriol Vinyals and George E Dahl. 2017. Neural message passing for quantum chemistry. In ICML.  Justin Gilmer Samuel S Schoenholz Patrick F Riley Oriol Vinyals and George E Dahl. 2017. Neural message passing for quantum chemistry. In ICML."},{"key":"e_1_3_2_2_12_1","volume-title":"A baseline for detecting misclassified and out-of-distribution examples in neural networks. ICLR","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. ICLR ( 2017 ). Dan Hendrycks and Kevin Gimpel. 2017. A baseline for detecting misclassified and out-of-distribution examples in neural networks. ICLR (2017)."},{"key":"e_1_3_2_2_13_1","volume-title":"Distilling the knowledge in a neural network. NeurIPS","author":"Hinton Geoffrey","year":"2014","unstructured":"Geoffrey Hinton , Oriol Vinyals , and Jeff Dean . 2014. Distilling the knowledge in a neural network. NeurIPS ( 2014 ). Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2014. Distilling the knowledge in a neural network. NeurIPS (2014)."},{"key":"e_1_3_2_2_14_1","first-page":"22118","article-title":"Open graph benchmark: Datasets for machine learning on graphs","volume":"33","author":"Hu Weihua","year":"2020","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 . NeurIPS , Vol. 33 (2020), 22118 -- 22133 . 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. NeurIPS, Vol. 33 (2020), 22118--22133.","journal-title":"NeurIPS"},{"key":"e_1_3_2_2_15_1","first-page":"677","article-title":"On the importance of gradients for detecting distributional shifts in the wild","volume":"34","author":"Huang Rui","year":"2021","unstructured":"Rui Huang , Andrew Geng , and Yixuan Li . 2021 . On the importance of gradients for detecting distributional shifts in the wild . NeurIPS , Vol. 34 (2021), 677 -- 689 . Rui Huang, Andrew Geng, and Yixuan Li. 2021. On the importance of gradients for detecting distributional shifts in the wild. NeurIPS, Vol. 34 (2021), 677--689.","journal-title":"NeurIPS"},{"key":"e_1_3_2_2_16_1","volume-title":"Empowering graph representation learning with test-time graph transformation. arXiv preprint arXiv:2210.03561","author":"Jin Wei","year":"2022","unstructured":"Wei Jin , Tong Zhao , Jiayuan Ding , Yozen Liu , Jiliang Tang , and Neil Shah . 2022. Empowering graph representation learning with test-time graph transformation. arXiv preprint arXiv:2210.03561 ( 2022 ). Wei Jin, Tong Zhao, Jiayuan Ding, Yozen Liu, Jiliang Tang, and Neil Shah. 2022. Empowering graph representation learning with test-time graph transformation. arXiv preprint arXiv:2210.03561 (2022)."},{"key":"e_1_3_2_2_17_1","volume-title":"Semi-supervised classification with graph convolutional networks. ICLR","author":"Kipf Thomas N","year":"2017","unstructured":"Thomas N Kipf and Max Welling . 2017. Semi-supervised classification with graph convolutional networks. ICLR ( 2017 ). Thomas N Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. ICLR (2017)."},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0933-3657(03)00043-5"},{"key":"e_1_3_2_2_19_1","volume-title":"NeurIPS","volume":"31","author":"Lee Kimin","year":"2018","unstructured":"Kimin Lee , Kibok Lee , Honglak Lee , and Jinwoo Shin . 2018 . A simple unified framework for detecting out-of-distribution samples and adversarial attacks . NeurIPS , Vol. 31 (2018). Kimin Lee, Kibok Lee, Honglak Lee, and Jinwoo Shin. 2018. A simple unified framework for detecting out-of-distribution samples and adversarial attacks. NeurIPS, Vol. 31 (2018)."},{"key":"e_1_3_2_2_20_1","first-page":"30277","article-title":"Graphde: A generative framework for debiased learning and out-of-distribution detection on graphs","volume":"35","author":"Li Zenan","year":"2022","unstructured":"Zenan Li , Qitian Wu , Fan Nie , and Junchi Yan . 2022 . Graphde: A generative framework for debiased learning and out-of-distribution detection on graphs . Advances in Neural Information Processing Systems , Vol. 35 (2022), 30277 -- 30290 . Zenan Li, Qitian Wu, Fan Nie, and Junchi Yan. 2022. Graphde: A generative framework for debiased learning and out-of-distribution detection on graphs. Advances in Neural Information Processing Systems, Vol. 35 (2022), 30277--30290.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_21_1","volume-title":"Enhancing the reliability of out-of-distribution image detection in neural networks. ICLR","author":"Liang Shiyu","year":"2018","unstructured":"Shiyu Liang , Yixuan Li , and Rayadurgam Srikant . 2018. Enhancing the reliability of out-of-distribution image detection in neural networks. ICLR ( 2018 ). Shiyu Liang, Yixuan Li, and Rayadurgam Srikant. 2018. Enhancing the reliability of out-of-distribution image detection in neural networks. ICLR (2018)."},{"key":"e_1_3_2_2_22_1","first-page":"1","article-title":"c. Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing","volume":"55","author":"Liu Pengfei","year":"2023","unstructured":"Pengfei Liu , Weizhe Yuan , Jinlan Fu , Zhengbao Jiang , Hiroaki Hayashi , and Graham Neubig . 2023 c. Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing . Comput. Surveys , Vol. 55 , 9 (2023), 1 -- 35 . Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2023 c. Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. Comput. Surveys, Vol. 55, 9 (2023), 1--35.","journal-title":"Comput. Surveys"},{"key":"e_1_3_2_2_23_1","first-page":"21464","article-title":"Energy-based out-of-distribution detection","volume":"33","author":"Liu Weitang","year":"2020","unstructured":"Weitang Liu , Xiaoyun Wang , John Owens , and Yixuan Li . 2020 . Energy-based out-of-distribution detection . NeurIPS , Vol. 33 (2020), 21464 -- 21475 . Weitang Liu, Xiaoyun Wang, John Owens, and Yixuan Li. 2020. Energy-based out-of-distribution detection. NeurIPS, Vol. 33 (2020), 21464--21475.","journal-title":"NeurIPS"},{"key":"e_1_3_2_2_24_1","volume-title":"2023 a. GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection. WSDM","author":"Liu Yixin","year":"2023","unstructured":"Yixin Liu , Kaize Ding , Huan Liu , and Shirui Pan . 2023 a. GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection. WSDM ( 2023 ). Yixin Liu, Kaize Ding, Huan Liu, and Shirui Pan. 2023 a. GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection. WSDM (2023)."},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3068344"},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583386"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3488560.3498389"},{"key":"e_1_3_2_2_28_1","unstructured":"Rongrong Ma Guansong Pang Ling Chen and Anton van den Hengel. 2022. Deep Graph-level Anomaly Detection by Glocal Knowledge Distillation. In WSDM. 704--714.  Rongrong Ma Guansong Pang Ling Chen and Anton van den Hengel. 2022. Deep Graph-level Anomaly Detection by Glocal Knowledge Distillation. In WSDM. 704--714."},{"key":"e_1_3_2_2_29_1","volume-title":"NeurIPS","volume":"32","author":"Maron Haggai","year":"2019","unstructured":"Haggai Maron , Heli Ben-Hamu , Hadar Serviansky , and Yaron Lipman . 2019 a. Provably powerful graph networks . NeurIPS , Vol. 32 (2019). Haggai Maron, Heli Ben-Hamu, Hadar Serviansky, and Yaron Lipman. 2019a. Provably powerful graph networks. NeurIPS, Vol. 32 (2019)."},{"key":"e_1_3_2_2_30_1","volume-title":"Invariant and equivariant graph networks. ICLR","author":"Maron Haggai","year":"2019","unstructured":"Haggai Maron , Heli Ben-Hamu , Nadav Shamir , and Yaron Lipman . 2019b. Invariant and equivariant graph networks. ICLR ( 2019 ). Haggai Maron, Heli Ben-Hamu, Nadav Shamir, and Yaron Lipman. 2019b. Invariant and equivariant graph networks. ICLR (2019)."},{"key":"e_1_3_2_2_31_1","volume-title":"Tudataset: A collection of benchmark datasets for learning with graphs. arXiv preprint arXiv:2007.08663","author":"Morris Christopher","year":"2020","unstructured":"Christopher Morris , Nils M Kriege , Franka Bause , Kristian Kersting , Petra Mutzel , and Marion Neumann . 2020 . Tudataset: A collection of benchmark datasets for learning with graphs. arXiv preprint arXiv:2007.08663 (2020). Christopher Morris, Nils M Kriege, Franka Bause, Kristian Kersting, Petra Mutzel, and Marion Neumann. 2020. Tudataset: A collection of benchmark datasets for learning with graphs. arXiv preprint arXiv:2007.08663 (2020)."},{"key":"e_1_3_2_2_32_1","volume-title":"Dilan Gorur, and Balaji Lakshminarayanan.","author":"Nalisnick Eric","year":"2019","unstructured":"Eric Nalisnick , Akihiro Matsukawa , Yee Whye Teh , Dilan Gorur, and Balaji Lakshminarayanan. 2019 . Do deep generative models know what they don't know? ICLR ( 2019). Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, and Balaji Lakshminarayanan. 2019. Do deep generative models know what they don't know? ICLR (2019)."},{"key":"e_1_3_2_2_33_1","volume-title":"NeurIPS","volume":"32","author":"Ren Jie","year":"2019","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 . NeurIPS , Vol. 32 (2019). 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. NeurIPS, Vol. 32 (2019)."},{"key":"e_1_3_2_2_34_1","volume-title":"Alexander Binder, Emmanuel M\u00fcller, and Marius Kloft.","author":"Ruff Lukas","year":"2018","unstructured":"Lukas Ruff , Robert Vandermeulen , Nico Goernitz , Lucas Deecke , Shoaib Ahmed Siddiqui , Alexander Binder, Emmanuel M\u00fcller, and Marius Kloft. 2018 . Deep one-class classification. In ICML. PMLR , 4393--4402. Lukas Ruff, Robert Vandermeulen, Nico Goernitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Alexander Binder, Emmanuel M\u00fcller, and Marius Kloft. 2018. Deep one-class classification. In ICML. PMLR, 4393--4402."},{"key":"e_1_3_2_2_35_1","volume-title":"Unsupervised anomaly detection with generative adversarial networks to guide marker discovery","author":"Schlegl Thomas","unstructured":"Thomas Schlegl , Philipp Seeb\u00f6ck , Sebastian M Waldstein , Ursula Schmidt-Erfurth , and Georg Langs . 2017. Unsupervised anomaly detection with generative adversarial networks to guide marker discovery . In MICCAI. Springer , 146--157. Thomas Schlegl, Philipp Seeb\u00f6ck, Sebastian M Waldstein, Ursula Schmidt-Erfurth, and Georg Langs. 2017. Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In MICCAI. Springer, 146--157."},{"key":"e_1_3_2_2_36_1","volume-title":"Ssd: A unified framework for self-supervised outlier detection. ICLR","author":"Sehwag Vikash","year":"2021","unstructured":"Vikash Sehwag , Mung Chiang , and Prateek Mittal . 2021 . Ssd: A unified framework for self-supervised outlier detection. ICLR (2021). Vikash Sehwag, Mung Chiang, and Prateek Mittal. 2021. Ssd: A unified framework for self-supervised outlier detection. ICLR (2021)."},{"key":"e_1_3_2_2_37_1","volume-title":"Input complexity and out-of-distribution detection with likelihood-based generative models. ICLR","author":"Serr\u00e0 Joan","year":"2020","unstructured":"Joan Serr\u00e0 , David \u00c1lvarez , Vicencc G\u00f3mez , Olga Slizovskaia , Jos\u00e9 F N\u00fanez , and Jordi Luque . 2020. Input complexity and out-of-distribution detection with likelihood-based generative models. ICLR ( 2020 ). Joan Serr\u00e0, David \u00c1lvarez, Vicencc G\u00f3mez, Olga Slizovskaia, Jos\u00e9 F N\u00fanez, and Jordi Luque. 2020. Input complexity and out-of-distribution detection with likelihood-based generative models. ICLR (2020)."},{"key":"e_1_3_2_2_38_1","volume-title":"Towards out-of-distribution generalization: A survey. arXiv preprint arXiv:2108.13624","author":"Shen Zheyan","year":"2021","unstructured":"Zheyan Shen , Jiashuo Liu , Yue He , Xingxuan Zhang , Renzhe Xu , Han Yu , and Peng Cui . 2021. Towards out-of-distribution generalization: A survey. arXiv preprint arXiv:2108.13624 ( 2021 ). Zheyan Shen, Jiashuo Liu, Yue He, Xingxuan Zhang, Renzhe Xu, Han Yu, and Peng Cui. 2021. Towards out-of-distribution generalization: A survey. arXiv preprint arXiv:2108.13624 (2021)."},{"key":"e_1_3_2_2_39_1","first-page":"18033","article-title":"Graph posterior network: Bayesian predictive uncertainty for node classification","volume":"34","author":"Stadler Maximilian","year":"2021","unstructured":"Maximilian Stadler , Bertrand Charpentier , Simon Geisler , Daniel Z\u00fcgner , and Stephan G\u00fcnnemann . 2021 . Graph posterior network: Bayesian predictive uncertainty for node classification . NeurIPS , Vol. 34 (2021), 18033 -- 18048 . Maximilian Stadler, Bertrand Charpentier, Simon Geisler, Daniel Z\u00fcgner, and Stephan G\u00fcnnemann. 2021. Graph posterior network: Bayesian predictive uncertainty for node classification. NeurIPS, Vol. 34 (2021), 18033--18048.","journal-title":"NeurIPS"},{"key":"e_1_3_2_2_40_1","volume-title":"Gppt: Graph pre-training and prompt tuning to generalize graph neural networks. In ACM SIGKDD.","author":"Sun Mingchen","year":"2022","unstructured":"Mingchen Sun , Kaixiong Zhou , Xin He , Ying Wang , and Xin Wang . 2022 . Gppt: Graph pre-training and prompt tuning to generalize graph neural networks. In ACM SIGKDD. Mingchen Sun, Kaixiong Zhou, Xin He, Ying Wang, and Xin Wang. 2022. Gppt: Graph pre-training and prompt tuning to generalize graph neural networks. In ACM SIGKDD."},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1021\/ci034143r"},{"key":"e_1_3_2_2_42_1","volume-title":"JMLR","volume":"9","author":"der Maaten Laurens Van","year":"2008","unstructured":"Laurens Van der Maaten and Geoffrey Hinton . 2008 . Visualizing data using t-SNE . JMLR , Vol. 9 , 11 (2008). Laurens Van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. JMLR, Vol. 9, 11 (2008)."},{"key":"e_1_3_2_2_43_1","volume-title":"Graph attention networks. ICLR","author":"Veli\u010dkovi\u0107 Petar","year":"2018","unstructured":"Petar Veli\u010dkovi\u0107 , Guillem Cucurull , Arantxa Casanova , Adriana Romero , Pietro Lio , and Yoshua Bengio . 2018. Graph attention networks. ICLR ( 2018 ). Petar Veli\u010dkovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2018. Graph attention networks. ICLR (2018)."},{"key":"e_1_3_2_2_44_1","first-page":"4","article-title":"Deep graph infomax","volume":"2","author":"Veli\u010dkovi\u0107 Petar","year":"2019","unstructured":"Petar Veli\u010dkovi\u0107 , William Fedus , William L Hamilton , Pietro Li\u00f2 , Yoshua Bengio , and R Devon Hjelm . 2019 . Deep graph infomax . ICLR , Vol. 2 , 3 (2019), 4 . Petar Veli\u010dkovi\u0107, William Fedus, William L Hamilton, Pietro Li\u00f2, Yoshua Bengio, and R Devon Hjelm. 2019. Deep graph infomax. ICLR, Vol. 2, 3 (2019), 4.","journal-title":"ICLR"},{"key":"e_1_3_2_2_45_1","volume-title":"Out-of-distribution detection in classifiers via generation. NeurIPS","author":"Vernekar Sachin","year":"2019","unstructured":"Sachin Vernekar , Ashish Gaurav , Vahdat Abdelzad , Taylor Denouden , Rick Salay , and Krzysztof Czarnecki . 2019. Out-of-distribution detection in classifiers via generation. NeurIPS ( 2019 ). Sachin Vernekar, Ashish Gaurav, Vahdat Abdelzad, Taylor Denouden, Rick Salay, and Krzysztof Czarnecki. 2019. Out-of-distribution detection in classifiers via generation. NeurIPS (2019)."},{"key":"e_1_3_2_2_46_1","volume-title":"Watermarking for Out-of-distribution Detection. NeurIPS","author":"Wang Qizhou","year":"2022","unstructured":"Qizhou Wang , Feng Liu , Yonggang Zhang , Jing Zhang , Chen Gong , Tongliang Liu , and Bo Han . 2022. Watermarking for Out-of-distribution Detection. NeurIPS ( 2022 ). Qizhou Wang, Feng Liu, Yonggang Zhang, Jing Zhang, Chen Gong, Tongliang Liu, and Bo Han. 2022. Watermarking for Out-of-distribution Detection. NeurIPS (2022)."},{"key":"e_1_3_2_2_47_1","first-page":"12","article-title":"The reduction of a graph to canonical form and the algebra which appears therein. nti","volume":"2","author":"Weisfeiler Boris","year":"1968","unstructured":"Boris Weisfeiler and Andrei Leman . 1968 . The reduction of a graph to canonical form and the algebra which appears therein. nti , Series , Vol. 2 , 9 (1968), 12 -- 16 . Boris Weisfeiler and Andrei Leman. 1968. The reduction of a graph to canonical form and the algebra which appears therein. nti, Series, Vol. 2, 9 (1968), 12--16.","journal-title":"Series"},{"key":"e_1_3_2_2_48_1","volume-title":"Energy-based Out-of-Distribution Detection for Graph Neural Networks. arXiv preprint arXiv:2302.02914","author":"Wu Qitian","year":"2023","unstructured":"Qitian Wu , Yiting Chen , Chenxiao Yang , and Junchi Yan . 2023. Energy-based Out-of-Distribution Detection for Graph Neural Networks. arXiv preprint arXiv:2302.02914 ( 2023 ). Qitian Wu, Yiting Chen, Chenxiao Yang, and Junchi Yan. 2023. Energy-based Out-of-Distribution Detection for Graph Neural Networks. arXiv preprint arXiv:2302.02914 (2023)."},{"key":"e_1_3_2_2_49_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 , Vol. 9 , 2 ( 2018 ), 513--530. 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, Vol. 9, 2 (2018), 513--530."},{"key":"e_1_3_2_2_50_1","volume-title":"How powerful are graph neural networks? ICLR","author":"Xu Keyulu","year":"2019","unstructured":"Keyulu Xu , Weihua Hu , Jure Leskovec , and Stefanie Jegelka . 2019. How powerful are graph neural networks? ICLR ( 2019 ). Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019. How powerful are graph neural networks? ICLR (2019)."},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"crossref","unstructured":"Pinar Yanardag and SVN Vishwanathan. 2015. Deep graph kernels. In SIGKDD. 1365--1374.  Pinar Yanardag and SVN Vishwanathan. 2015. Deep graph kernels. In SIGKDD. 1365--1374.","DOI":"10.1145\/2783258.2783417"},{"key":"e_1_3_2_2_52_1","first-page":"28877","article-title":"Do transformers really perform badly for graph representation","volume":"34","author":"Ying Chengxuan","year":"2021","unstructured":"Chengxuan Ying , Tianle Cai , Shengjie Luo , Shuxin Zheng , Guolin Ke , Di He , Yanming Shen , and Tie-Yan Liu . 2021 . Do transformers really perform badly for graph representation ? NeurIPS , Vol. 34 (2021), 28877 -- 28888 . Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, and Tie-Yan Liu. 2021. Do transformers really perform badly for graph representation? NeurIPS, Vol. 34 (2021), 28877--28888.","journal-title":"NeurIPS"},{"key":"e_1_3_2_2_53_1","unstructured":"Yuning You Tianlong Chen Yang Shen and Zhangyang Wang. 2021. Graph contrastive learning automated. In ICML. PMLR 12121--12132.  Yuning You Tianlong Chen Yang Shen and Zhangyang Wang. 2021. Graph contrastive learning automated. In ICML. PMLR 12121--12132."},{"key":"e_1_3_2_2_54_1","first-page":"5812","article-title":"Graph contrastive learning with augmentations","volume":"33","author":"You Yuning","year":"2020","unstructured":"Yuning You , Tianlong Chen , Yongduo Sui , Ting Chen , Zhangyang Wang , and Yang Shen . 2020 . Graph contrastive learning with augmentations . NeurIPS , Vol. 33 (2020), 5812 -- 5823 . Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. 2020. Graph contrastive learning with augmentations. NeurIPS, Vol. 33 (2020), 5812--5823.","journal-title":"NeurIPS"},{"key":"e_1_3_2_2_55_1","volume-title":"Kwei-Herng Lai, Fan Yang, Zhimeng Jiang, Shaochen Zhong, and Xia Hu.","author":"Zha Daochen","year":"2023","unstructured":"Daochen Zha , Zaid Pervaiz Bhat , Kwei-Herng Lai, Fan Yang, Zhimeng Jiang, Shaochen Zhong, and Xia Hu. 2023 . Data-centric artificial intelligence: A survey. arXiv preprint arXiv:2303.10158 (2023). Daochen Zha, Zaid Pervaiz Bhat, Kwei-Herng Lai, Fan Yang, Zhimeng Jiang, Shaochen Zhong, and Xia Hu. 2023. Data-centric artificial intelligence: A survey. arXiv preprint arXiv:2303.10158 (2023)."},{"key":"e_1_3_2_2_56_1","doi-asserted-by":"publisher","DOI":"10.1145\/3446776"},{"key":"e_1_3_2_2_57_1","volume-title":"On using classification datasets to evaluate graph outlier detection: Peculiar observations and new insights. Big Data","author":"Zhao Lingxiao","year":"2021","unstructured":"Lingxiao Zhao and Leman Akoglu . 2021. On using classification datasets to evaluate graph outlier detection: Peculiar observations and new insights. Big Data ( 2021 ). Lingxiao Zhao and Leman Akoglu. 2021. On using classification datasets to evaluate graph outlier detection: Peculiar observations and new insights. Big Data (2021)."},{"key":"e_1_3_2_2_58_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 . NeurIPS , Vol. 33 (2020), 12827 -- 12836 . Xujiang Zhao, Feng Chen, Shu Hu, and Jin-Hee Cho. 2020. Uncertainty aware semi-supervised learning on graph data. NeurIPS, Vol. 33 (2020), 12827--12836.","journal-title":"NeurIPS"},{"key":"e_1_3_2_2_59_1","volume-title":"Contrastive out-of-distribution detection for pretrained transformers. EMNLP","author":"Zhou Wenxuan","year":"2021","unstructured":"Wenxuan Zhou , Fangyu Liu , and Muhao Chen . 2021. Contrastive out-of-distribution detection for pretrained transformers. EMNLP ( 2021 ). Wenxuan Zhou, Fangyu Liu, and Muhao Chen. 2021. Contrastive out-of-distribution detection for pretrained transformers. EMNLP (2021)."},{"key":"e_1_3_2_2_60_1","doi-asserted-by":"crossref","unstructured":"Ev Zisselman and Aviv Tamar. 2020. Deep residual flow for out of distribution detection. In CVPR. 13994--14003.  Ev Zisselman and Aviv Tamar. 2020. Deep residual flow for out of distribution detection. In CVPR. 13994--14003.","DOI":"10.1109\/CVPR42600.2020.01401"}],"event":{"name":"KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Long Beach CA USA","acronym":"KDD '23","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 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3580305.3599244","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3580305.3599244","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:51:15Z","timestamp":1750182675000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3580305.3599244"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,4]]},"references-count":60,"alternative-id":["10.1145\/3580305.3599244","10.1145\/3580305"],"URL":"https:\/\/doi.org\/10.1145\/3580305.3599244","relation":{},"subject":[],"published":{"date-parts":[[2023,8,4]]},"assertion":[{"value":"2023-08-04","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}