{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T15:17:35Z","timestamp":1783437455832,"version":"3.54.6"},"publisher-location":"New York, NY, USA","reference-count":61,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T00:00:00Z","timestamp":1724457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"International Science and Technology Cooperation Program of Jilin Province","award":["No.20230402076GH, No. 20240402067GH"],"award-info":[{"award-number":["No.20230402076GH, No. 20240402067GH"]}]},{"name":"Science and Technology Development Program of Jilin Province","award":["No. 20220201153GX"],"award-info":[{"award-number":["No. 20220201153GX"]}]},{"name":"Foundation of the National Key Research and Development of China","award":["No.2021ZD0112500"],"award-info":[{"award-number":["No.2021ZD0112500"]}]},{"name":"National Natural Science Foundation of China","award":["No.62372211, 62272191"],"award-info":[{"award-number":["No.62372211, 62272191"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,8,25]]},"DOI":"10.1145\/3637528.3671785","type":"proceedings-article","created":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T04:55:12Z","timestamp":1724561712000},"page":"2640-2650","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["Optimizing OOD Detection in Molecular Graphs: A Novel Approach with Diffusion Models"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0403-0103","authenticated-orcid":false,"given":"Xu","family":"Shen","sequence":"first","affiliation":[{"name":"Jilin University, Changchun, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0845-9521","authenticated-orcid":false,"given":"Yili","family":"Wang","sequence":"additional","affiliation":[{"name":"Jilin University, Changchun, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5226-8736","authenticated-orcid":false,"given":"Kaixiong","family":"Zhou","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology, Cambridge, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0794-527X","authenticated-orcid":false,"given":"Shirui","family":"Pan","sequence":"additional","affiliation":[{"name":"Griffith University, Goldcoast, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9448-7689","authenticated-orcid":false,"given":"Xin","family":"Wang","sequence":"additional","affiliation":[{"name":"Jilin University, Changchun, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,8,24]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Equivariant subgraph aggregation networks. arXiv preprint arXiv:2110.02910","author":"Bevilacqua Beatrice","year":"2021","unstructured":"Beatrice Bevilacqua, Fabrizio Frasca, Derek Lim, Balasubramaniam Srinivasan, Chen Cai, Gopinath Balamurugan, Michael M Bronstein, and Haggai Maron. 2021. Equivariant subgraph aggregation networks. arXiv preprint arXiv:2110.02910 (2021)."},{"key":"e_1_3_2_2_2_1","first-page":"5103","article-title":"Line graph neural networks for link prediction","volume":"44","author":"Cai Lei","year":"2021","unstructured":"Lei Cai, Jundong Li, Jie Wang, and Shuiwang Ji. 2021. Line graph neural networks for link prediction. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 44, 9 (2021), 5103--5113.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_2_2_3_1","unstructured":"Xiaohui Chen Jiaxing He Xu Han and Liping Liu. 2023. Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling. (2023)."},{"key":"e_1_3_2_2_4_1","volume-title":"Radu Tudor Ionescu, and Mubarak Shah","author":"Croitoru Florinel-Alin","year":"2023","unstructured":"Florinel-Alin Croitoru, Vlad Hondru, Radu Tudor Ionescu, and Mubarak Shah. 2023. Diffusion models in vision: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence (2023)."},{"key":"e_1_3_2_2_5_1","first-page":"4776","article-title":"How powerful are k-hop message passing graph neural networks","volume":"35","author":"Feng Jiarui","year":"2022","unstructured":"Jiarui Feng, Yixin Chen, Fuhai Li, Anindya Sarkar, and Muhan Zhang. 2022. How powerful are k-hop message passing graph neural networks. Advances in Neural Information Processing Systems, Vol. 35 (2022), 4776--4790.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00152"},{"key":"e_1_3_2_2_7_1","volume-title":"International conference on machine learning. PMLR, 1263--1272","author":"Gilmer Justin","year":"2017","unstructured":"Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl. 2017. Neural message passing for quantum chemistry. In International conference on machine learning. PMLR, 1263--1272."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW59228.2023.00296"},{"key":"e_1_3_2_2_9_1","first-page":"2059","article-title":"Good: A graph out-of-distribution benchmark","volume":"35","author":"Gui Shurui","year":"2022","unstructured":"Shurui Gui, Xiner Li, Limei Wang, and Shuiwang Ji. 2022. Good: A graph out-of-distribution benchmark. Advances in Neural Information Processing Systems, Vol. 35 (2022), 2059--2073.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i4.20316"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"crossref","unstructured":"Yuxin Guo Cheng Yang Yuluo Chen Jixi Liu Chuan Shi and Junping Du. 2023. A Data-centric Framework to Endow Graph Neural Networks with Out-Of-Distribution Detection Ability. (2023).","DOI":"10.1145\/3580305.3599244"},{"key":"e_1_3_2_2_12_1","volume-title":"Inductive representation learning on large graphs. Advances in neural information processing systems","author":"Hamilton Will","year":"2017","unstructured":"Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_2_13_1","first-page":"14239","article-title":"Bernnet: Learning arbitrary graph spectral filters via bernstein approximation","volume":"34","author":"He Mingguo","year":"2021","unstructured":"Mingguo He, Zhewei Wei, Hongteng Xu, et al. 2021. Bernnet: Learning arbitrary graph spectral filters via bernstein approximation. Advances in Neural Information Processing Systems, Vol. 34 (2021), 14239--14251.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_14_1","volume-title":"A baseline for detecting misclassified and out-of-distribution examples in neural networks. arXiv preprint arXiv:1610.02136","author":"Hendrycks Dan","year":"2016","unstructured":"Dan Hendrycks and Kevin Gimpel. 2016. A baseline for detecting misclassified and out-of-distribution examples in neural networks. arXiv preprint arXiv:1610.02136 (2016)."},{"key":"e_1_3_2_2_15_1","volume-title":"Graphs in molecular biology. BMC bioinformatics","author":"Huber Wolfgang","year":"2007","unstructured":"Wolfgang Huber, Vincent J Carey, Li Long, Seth Falcon, and Robert Gentleman. 2007. Graphs in molecular biology. BMC bioinformatics, Vol. 8, 6 (2007), 1--14."},{"key":"e_1_3_2_2_16_1","volume-title":"Generative models for graph-based protein design. Advances in neural information processing systems","author":"Ingraham John","year":"2019","unstructured":"John Ingraham, Vikas Garg, Regina Barzilay, and Tommi Jaakkola. 2019. Generative models for graph-based protein design. Advances in neural information processing systems, Vol. 32 (2019)."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i7.25970"},{"key":"e_1_3_2_2_18_1","volume-title":"International Conference on Machine Learning. PMLR, 10362--10383","author":"Jo Jaehyeong","year":"2022","unstructured":"Jaehyeong Jo, Seul Lee, and Sung Ju Hwang. 2022. Score-based generative modeling of graphs via the system of stochastic differential equations. In International Conference on Machine Learning. PMLR, 10362--10383."},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.118935"},{"key":"e_1_3_2_2_20_1","volume-title":"Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations.","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling. 2016. Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i9.17001"},{"key":"e_1_3_2_2_22_1","volume-title":"Learning graph-level representation for drug discovery. arXiv preprint arXiv:1709.03741","author":"Li Junying","year":"2017","unstructured":"Junying Li, Deng Cai, and Xiaofei He. 2017. Learning graph-level representation for drug discovery. arXiv preprint arXiv:1709.03741 (2017)."},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-018-0287-6"},{"key":"e_1_3_2_2_24_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.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_25_1","volume-title":"Sagess: Sampling graph denoising diffusion model for scalable graph generation. arXiv preprint arXiv:2306.16827","author":"Limnios Stratis","year":"2023","unstructured":"Stratis Limnios, Praveen Selvaraj, Mihai Cucuringu, Carsten Maple, Gesine Reinert, and Andrew Elliott. 2023. Sagess: Sampling graph denoising diffusion model for scalable graph generation. arXiv preprint arXiv:2306.16827 (2023)."},{"key":"e_1_3_2_2_26_1","unstructured":"Gary Liu Denise B Catacutan Khushi Rathod Kyle Swanson Wengong Jin Jody C Mohammed Anush Chiappino-Pepe Saad A Syed Meghan Fragis Kenneth Rachwalski et al. 2023. Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii. Nature Chemical Biology (2023) 1--9."},{"key":"e_1_3_2_2_27_1","volume-title":"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. Data-Centric Learning from Unlabeled Graphs with Diffusion Model. arXiv preprint arXiv:2303.10108 (2023)."},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3539597.3570446"},{"key":"e_1_3_2_2_29_1","volume-title":"Yufan Wang, and Kilian Q Weinberger. 2023 d. Unsupervised Out-of-Distribution Detection with Diffusion Inpainting. arXiv preprint arXiv:2302.10326","author":"Liu Zhenzhen","year":"2023","unstructured":"Zhenzhen Liu, Jin Peng Zhou, Yufan Wang, and Kilian Q Weinberger. 2023 d. Unsupervised Out-of-Distribution Detection with Diffusion Inpainting. arXiv preprint arXiv:2302.10326 (2023)."},{"key":"e_1_3_2_2_30_1","volume-title":"International Conference on Learning Representations.","author":"Liu Zirui","year":"2021","unstructured":"Zirui Liu, Kaixiong Zhou, Fan Yang, Li Li, Rui Chen, and Xia Hu. 2021. EXACT: Scalable graph neural networks training via extreme activation compression. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_31_1","volume-title":"Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining. 704--714","author":"Ma Rongrong","unstructured":"Rongrong Ma, Guansong Pang, Ling Chen, and Anton van den Hengel. 2022. Deep graph-level anomaly detection by glocal knowledge distillation. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining. 704--714."},{"key":"e_1_3_2_2_32_1","volume-title":"Rethinking Independent Cross-Entropy Loss For Graph-Structured Data. arXiv preprint arXiv:2405.15564","author":"Miao Rui","year":"2024","unstructured":"Rui Miao, Kaixiong Zhou, Yili Wang, Ninghao Liu, Ying Wang, and Xin Wang. 2024. Rethinking Independent Cross-Entropy Loss For Graph-Structured Data. arXiv preprint arXiv:2405.15564 (2024)."},{"key":"e_1_3_2_2_33_1","volume-title":"A graph vae and graph transformer approach to generating molecular graphs. arXiv preprint arXiv:2104.04345","author":"Mitton Joshua","year":"2021","unstructured":"Joshua Mitton, Hans M Senn, Klaas Wynne, and Roderick Murray-Smith. 2021. A graph vae and graph transformer approach to generating molecular graphs. arXiv preprint arXiv:2104.04345 (2021)."},{"key":"e_1_3_2_2_34_1","volume-title":"International Conference on Artificial Intelligence and Statistics. PMLR, 4474--4484","author":"Niu Chenhao","year":"2020","unstructured":"Chenhao Niu, Yang Song, Jiaming Song, Shengjia Zhao, Aditya Grover, and Stefano Ermon. 2020. Permutation invariant graph generation via score-based generative modeling. In International Conference on Artificial Intelligence and Statistics. PMLR, 4474--4484."},{"key":"e_1_3_2_2_35_1","volume-title":"Raising the bar in graph-level anomaly detection. arXiv preprint arXiv:2205.13845","author":"Qiu Chen","year":"2022","unstructured":"Chen Qiu, Marius Kloft, Stephan Mandt, and Maja Rudolph. 2022. Raising the bar in graph-level anomaly detection. arXiv preprint arXiv:2205.13845 (2022)."},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1038\/s43246-022-00315-6"},{"key":"e_1_3_2_2_37_1","volume-title":"International conference on machine learning. PMLR, 4393--4402","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 International conference on machine learning. PMLR, 4393--4402."},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3397692"},{"key":"e_1_3_2_2_39_1","first-page":"1415","article-title":"Maximum likelihood training of score-based diffusion models","volume":"34","author":"Song Yang","year":"2021","unstructured":"Yang Song, Conor Durkan, Iain Murray, and Stefano Ermon. 2021. Maximum likelihood training of score-based diffusion models. Advances in Neural Information Processing Systems, Vol. 34 (2021), 1415--1428.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_40_1","volume-title":"International Conference on Machine Learning. PMLR, 6275--6284","author":"Titouan Vayer","year":"2019","unstructured":"Vayer Titouan, Nicolas Courty, Romain Tavenard, and R\u00e9mi Flamary. 2019. Optimal transport for structured data with application on graphs. In International Conference on Machine Learning. PMLR, 6275--6284."},{"key":"e_1_3_2_2_41_1","volume-title":"The Eleventh International Conference on Learning Representations.","author":"Vignac Clement","year":"2022","unstructured":"Clement Vignac, Igor Krawczuk, Antoine Siraudin, Bohan Wang, Volkan Cevher, and Pascal Frossard. 2022. DiGress: Discrete Denoising diffusion for graph generation. In The Eleventh International Conference on Learning Representations."},{"key":"e_1_3_2_2_42_1","first-page":"11800","article-title":"Template based graph neural network with optimal transport distances","volume":"35","author":"Vincent-Cuaz C\u00e9dric","year":"2022","unstructured":"C\u00e9dric Vincent-Cuaz, R\u00e9mi Flamary, Marco Corneli, Titouan Vayer, and Nicolas Courty. 2022. Template based graph neural network with optimal transport distances. Advances in Neural Information Processing Systems, Vol. 35 (2022), 11800--11814.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_43_1","first-page":"23768","article-title":"Be confident! towards trustworthy graph neural networks via confidence calibration","volume":"34","author":"Wang Xiao","year":"2021","unstructured":"Xiao Wang, Hongrui Liu, Chuan Shi, and Cheng Yang. 2021. Be confident! towards trustworthy graph neural networks via confidence calibration. Advances in Neural Information Processing Systems, Vol. 34 (2021), 23768--23779.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380186"},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-022-00447-x"},{"key":"e_1_3_2_2_46_1","volume-title":"Efficient Sharpness-Aware Minimization for Molecular Graph Transformer Models. In The Twelfth International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=Od39h4XQ3Y","author":"Wang Yili","year":"2024","unstructured":"Yili Wang, Kaixiong Zhou, Ninghao Liu, Ying Wang, and Xin Wang. 2024. Efficient Sharpness-Aware Minimization for Molecular Graph Transformer Models. In The Twelfth International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=Od39h4XQ3Y"},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557228"},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ddtec.2020.11.009"},{"key":"e_1_3_2_2_49_1","unstructured":"Bingzhe Wu Jintang Li Junchi Yu Yatao Bian Hengtong Zhang CHaochao Chen Chengbin Hou Guoji Fu Liang Chen Tingyang Xu et al. 2022. A survey of trustworthy graph learning: Reliability explainability and privacy protection. arXiv preprint arXiv:2205.10014 (2022)."},{"key":"e_1_3_2_2_50_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)."},{"key":"e_1_3_2_2_51_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)."},{"key":"e_1_3_2_2_52_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2022.102946"},{"key":"e_1_3_2_2_53_1","volume-title":"From stars to subgraphs: Uplifting any GNN with local structure awareness. arXiv preprint arXiv:2110.03753","author":"Zhao Lingxiao","year":"2021","unstructured":"Lingxiao Zhao, Wei Jin, Leman Akoglu, and Neil Shah. 2021. From stars to subgraphs: Uplifting any GNN with local structure awareness. arXiv preprint arXiv:2110.03753 (2021)."},{"key":"e_1_3_2_2_54_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611977653.ch7"},{"key":"e_1_3_2_2_55_1","volume-title":"Towards deeper graph neural networks with differentiable group normalization. Advances in neural information processing systems","author":"Zhou Kaixiong","year":"2020","unstructured":"Kaixiong Zhou, Xiao Huang, Yuening Li, Daochen Zha, Rui Chen, and Xia Hu. 2020. Towards deeper graph neural networks with differentiable group normalization. Advances in neural information processing systems, Vol. 33 (2020), 4917--4928."},{"key":"e_1_3_2_2_56_1","volume-title":"Auto-gnn: Neural architecture search of graph neural networks. Frontiers in big Data","author":"Zhou Kaixiong","year":"2022","unstructured":"Kaixiong Zhou, Xiao Huang, Qingquan Song, Rui Chen, and Xia Hu. 2022. Auto-gnn: Neural architecture search of graph neural networks. Frontiers in big Data, Vol. 5 (2022), 1029307."},{"key":"e_1_3_2_2_57_1","first-page":"21834","article-title":"Dirichlet energy constrained learning for deep graph neural networks","volume":"34","author":"Zhou Kaixiong","year":"2021","unstructured":"Kaixiong Zhou, Xiao Huang, Daochen Zha, Rui Chen, Li Li, Soo-Hyun Choi, and Xia Hu. 2021. Dirichlet energy constrained learning for deep graph neural networks. Advances in Neural Information Processing Systems, Vol. 34 (2021), 21834--21846.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_58_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/336"},{"key":"e_1_3_2_2_59_1","volume-title":"Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence. 1352--1358","author":"Zhou Kaixiong","year":"2021","unstructured":"Kaixiong Zhou, Qingquan Song, Xiao Huang, Daochen Zha, Na Zou, and Xia Hu. 2021. Multi-channel graph neural networks. In Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence. 1352--1358."},{"key":"e_1_3_2_2_60_1","volume-title":"Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence. 1352--1358","author":"Zhou Kaixiong","year":"2021","unstructured":"Kaixiong Zhou, Qingquan Song, Xiao Huang, Daochen Zha, Na Zou, and Xia Hu. 2021. Multi-channel graph neural networks. In Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence. 1352--1358."},{"key":"e_1_3_2_2_61_1","volume-title":"Learning on Graphs Conference. PMLR, 47--1.","author":"Zhu Yanqiao","year":"2022","unstructured":"Yanqiao Zhu, Yuanqi Du, Yinkai Wang, Yichen Xu, Jieyu Zhang, Qiang Liu, and Shu Wu. 2022. A survey on deep graph generation: Methods and applications. In Learning on Graphs Conference. PMLR, 47--1."}],"event":{"name":"KDD '24: The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Barcelona Spain","acronym":"KDD '24","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 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671785","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3637528.3671785","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:04:14Z","timestamp":1750291454000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671785"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,24]]},"references-count":61,"alternative-id":["10.1145\/3637528.3671785","10.1145\/3637528"],"URL":"https:\/\/doi.org\/10.1145\/3637528.3671785","relation":{},"subject":[],"published":{"date-parts":[[2024,8,24]]},"assertion":[{"value":"2024-08-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}