{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T13:05:18Z","timestamp":1775912718356,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":51,"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":"Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT)","award":["No.2022-0-00157 and No.2022-0-00077"],"award-info":[{"award-number":["No.2022-0-00157 and No.2022-0-00077"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,8,6]]},"DOI":"10.1145\/3580305.3599515","type":"proceedings-article","created":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T18:10:58Z","timestamp":1691172658000},"page":"1120-1131","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Task-Equivariant Graph Few-shot Learning"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8605-2618","authenticated-orcid":false,"given":"Sungwon","family":"Kim","sequence":"first","affiliation":[{"name":"KAIST, Daejeon, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3874-1667","authenticated-orcid":false,"given":"Junseok","family":"Lee","sequence":"additional","affiliation":[{"name":"KAIST, Daejeon, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3995-1148","authenticated-orcid":false,"given":"Namkyeong","family":"Lee","sequence":"additional","affiliation":[{"name":"KAIST, Daejeon, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3353-8515","authenticated-orcid":false,"given":"Wonjoong","family":"Kim","sequence":"additional","affiliation":[{"name":"KAIST, Daejeon, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-7607-6206","authenticated-orcid":false,"given":"Seungyoon","family":"Choi","sequence":"additional","affiliation":[{"name":"KAIST, Daejeon, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5957-5816","authenticated-orcid":false,"given":"Chanyoung","family":"Park","sequence":"additional","affiliation":[{"name":"KAIST, Daejeon, Republic of Korea"}]}],"member":"320","published-online":{"date-parts":[[2023,8,4]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Truong Son Hy, and Risi Kondor","author":"Anderson Brandon","year":"2019","unstructured":"Brandon Anderson , Truong Son Hy, and Risi Kondor . 2019 . Cormorant : Covariant molecular neural networks. Advances in neural information processing systems, Vol. 32 (2019). Brandon Anderson, Truong Son Hy, and Risi Kondor. 2019. Cormorant: Covariant molecular neural networks. Advances in neural information processing systems, Vol. 32 (2019)."},{"key":"e_1_3_2_2_2_1","volume-title":"Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking. arXiv preprint arXiv:1707.03815","author":"Bojchevski Aleksandar","year":"2017","unstructured":"Aleksandar Bojchevski and Stephan G\u00fcnnemann . 2017. Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking. arXiv preprint arXiv:1707.03815 ( 2017 ). Aleksandar Bojchevski and Stephan G\u00fcnnemann. 2017. Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking. arXiv preprint arXiv:1707.03815 (2017)."},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00893"},{"key":"e_1_3_2_2_4_1","volume-title":"International conference on machine learning. PMLR, 2990--2999","author":"Cohen Taco","year":"2016","unstructured":"Taco Cohen and Max Welling . 2016 a. Group equivariant convolutional networks . In International conference on machine learning. PMLR, 2990--2999 . Taco Cohen and Max Welling. 2016a. Group equivariant convolutional networks. In International conference on machine learning. PMLR, 2990--2999."},{"key":"e_1_3_2_2_5_1","volume-title":"Steerable cnns. arXiv preprint arXiv:1612.08498","author":"Cohen Taco S","year":"2016","unstructured":"Taco S Cohen and Max Welling . 2016b. Steerable cnns. arXiv preprint arXiv:1612.08498 ( 2016 ). Taco S Cohen and Max Welling. 2016b. Steerable cnns. arXiv preprint arXiv:1612.08498 (2016)."},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411922"},{"key":"e_1_3_2_2_7_1","volume-title":"International conference on machine learning. PMLR, 1126--1135","author":"Finn Chelsea","year":"2017","unstructured":"Chelsea Finn , Pieter Abbeel , and Sergey Levine . 2017 . Model-agnostic meta-learning for fast adaptation of deep networks . In International conference on machine learning. PMLR, 1126--1135 . Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017. Model-agnostic meta-learning for fast adaptation of deep networks. In International conference on machine learning. PMLR, 1126--1135."},{"key":"e_1_3_2_2_8_1","first-page":"5862","article-title":"Graph meta learning via local subgraphs","volume":"33","author":"Huang Kexin","year":"2020","unstructured":"Kexin Huang and Marinka Zitnik . 2020 . Graph meta learning via local subgraphs . Advances in Neural Information Processing Systems , Vol. 33 (2020), 5862 -- 5874 . Kexin Huang and Marinka Zitnik. 2020. Graph meta learning via local subgraphs. Advances in Neural Information Processing Systems, Vol. 33 (2020), 5862--5874.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_9_1","volume-title":"Heterogeneous Graph Learning for Multi-modal Medical Data Analysis. arXiv preprint arXiv:2211.15158","author":"Kim Sein","year":"2022","unstructured":"Sein Kim , Namkyeong Lee , Junseok Lee , Dongmin Hyun , and Chanyoung Park . 2022. Heterogeneous Graph Learning for Multi-modal Medical Data Analysis. arXiv preprint arXiv:2211.15158 ( 2022 ). Sein Kim, Namkyeong Lee, Junseok Lee, Dongmin Hyun, and Chanyoung Park. 2022. Heterogeneous Graph Learning for Multi-modal Medical Data Analysis. arXiv preprint arXiv:2211.15158 (2022)."},{"key":"e_1_3_2_2_10_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_11_1","volume-title":"Equivariant flows: sampling configurations for multi-body systems with symmetric energies. arXiv preprint arXiv:1910.00753","author":"K\u00f6hler Jonas","year":"2019","unstructured":"Jonas K\u00f6hler , Leon Klein , and Frank No\u00e9 . 2019. Equivariant flows: sampling configurations for multi-body systems with symmetric energies. arXiv preprint arXiv:1910.00753 ( 2019 ). Jonas K\u00f6hler, Leon Klein, and Frank No\u00e9. 2019. Equivariant flows: sampling configurations for multi-body systems with symmetric energies. arXiv preprint arXiv:1910.00753 (2019)."},{"key":"e_1_3_2_2_12_1","volume-title":"International conference on machine learning. PMLR, 5361--5370","author":"K\u00f6hler Jonas","year":"2020","unstructured":"Jonas K\u00f6hler , Leon Klein , and Frank No\u00e9 . 2020 . Equivariant flows: exact likelihood generative learning for symmetric densities . In International conference on machine learning. PMLR, 5361--5370 . Jonas K\u00f6hler, Leon Klein, and Frank No\u00e9. 2020. Equivariant flows: exact likelihood generative learning for symmetric densities. In International conference on machine learning. PMLR, 5361--5370."},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531838"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557428"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i7.20700"},{"key":"e_1_3_2_2_16_1","volume-title":"Veloso","author":"Li Xiaoxiao","year":"2019","unstructured":"Xiaoxiao Li , Jo\u00e3o Sa\u00fade , Prashant P. Reddy , and Manuela M . Veloso . 2019 . Classifying and Understanding Financial Data Using Graph Neural Network . Xiaoxiao Li, Jo\u00e3o Sa\u00fade, Prashant P. Reddy, and Manuela M. Veloso. 2019. Classifying and Understanding Financial Data Using Graph Neural Network."},{"key":"e_1_3_2_2_17_1","volume-title":"Meta-sgd: Learning to learn quickly for few-shot learning. arXiv preprint arXiv:1707.09835","author":"Li Zhenguo","year":"2017","unstructured":"Zhenguo Li , Fengwei Zhou , Fei Chen , and Hang Li . 2017 . Meta-sgd: Learning to learn quickly for few-shot learning. arXiv preprint arXiv:1707.09835 (2017). Zhenguo Li, Fengwei Zhou, Fei Chen, and Hang Li. 2017. Meta-sgd: Learning to learn quickly for few-shot learning. arXiv preprint arXiv:1707.09835 (2017)."},{"key":"e_1_3_2_2_18_1","volume-title":"Advances in Neural Information Processing Systems","volume":"32","author":"Liu Lu","year":"2019","unstructured":"Lu Liu , Tianyi Zhou , Guodong Long , Jing Jiang , and Chengqi Zhang . 2019 . Learning to propagate for graph meta-learning . Advances in Neural Information Processing Systems , Vol. 32 (2019). Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019. Learning to propagate for graph meta-learning. Advances in Neural Information Processing Systems, Vol. 32 (2019)."},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531978"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16551"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783381"},{"key":"e_1_3_2_2_22_1","volume-title":"Relevance of rotationally equivariant convolutions for predicting molecular properties. arXiv preprint arXiv:2008.08461","author":"Miller Benjamin Kurt","year":"2020","unstructured":"Benjamin Kurt Miller , Mario Geiger , Tess E Smidt , and Frank No\u00e9 . 2020. Relevance of rotationally equivariant convolutions for predicting molecular properties. arXiv preprint arXiv:2008.08461 ( 2020 ). Benjamin Kurt Miller, Mario Geiger, Tess E Smidt, and Frank No\u00e9. 2020. Relevance of rotationally equivariant convolutions for predicting molecular properties. arXiv preprint arXiv:2008.08461 (2020)."},{"key":"e_1_3_2_2_23_1","volume-title":"On first-order meta-learning algorithms. arXiv preprint arXiv:1803.02999","author":"Nichol Alex","year":"2018","unstructured":"Alex Nichol , Joshua Achiam , and John Schulman . 2018. On first-order meta-learning algorithms. arXiv preprint arXiv:1803.02999 ( 2018 ). Alex Nichol, Joshua Achiam, and John Schulman. 2018. On first-order meta-learning algorithms. arXiv preprint arXiv:1803.02999 (2018)."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1109\/TPAMI.2011.191","article-title":"Exploring context and content links in social media: A latent space method","volume":"34","author":"Qi Guo-Jun","year":"2011","unstructured":"Guo-Jun Qi , Charu Aggarwal , Qi Tian , Heng Ji , and Thomas Huang . 2011 . Exploring context and content links in social media: A latent space method . IEEE Transactions on Pattern Analysis and Machine Intelligence , Vol. 34 , 5 (2011), 850 -- 862 . Guo-Jun Qi, Charu Aggarwal, Qi Tian, Heng Ji, and Thomas Huang. 2011. Exploring context and content links in social media: A latent space method. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 34, 5 (2011), 850--862.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_2_2_25_1","unstructured":"Sachin Ravi and Hugo Larochelle. 2016. Optimization as a model for few-shot learning. (2016).  Sachin Ravi and Hugo Larochelle. 2016. Optimization as a model for few-shot learning. (2016)."},{"key":"e_1_3_2_2_26_1","volume-title":"International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=rJY0-Kcll","author":"Ravi Sachin","year":"2017","unstructured":"Sachin Ravi and Hugo Larochelle . 2017 . Optimization as a Model for Few-Shot Learning . In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=rJY0-Kcll Sachin Ravi and Hugo Larochelle. 2017. Optimization as a Model for Few-Shot Learning. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=rJY0-Kcll"},{"key":"e_1_3_2_2_27_1","volume-title":"Equivariant hamiltonian flows. arXiv preprint arXiv:1909.13739","author":"Rezende Danilo Jimenez","year":"2019","unstructured":"Danilo Jimenez Rezende , S\u00e9bastien Racani\u00e8re , Irina Higgins , and Peter Toth . 2019. Equivariant hamiltonian flows. arXiv preprint arXiv:1909.13739 ( 2019 ). Danilo Jimenez Rezende, S\u00e9bastien Racani\u00e8re, Irina Higgins, and Peter Toth. 2019. Equivariant hamiltonian flows. arXiv preprint arXiv:1909.13739 (2019)."},{"key":"e_1_3_2_2_28_1","volume-title":"Group equivariant stand-alone self-attention for vision. arXiv preprint arXiv:2010.00977","author":"Romero David W","year":"2020","unstructured":"David W Romero and Jean-Baptiste Cordonnier . 2020. Group equivariant stand-alone self-attention for vision. arXiv preprint arXiv:2010.00977 ( 2020 ). David W Romero and Jean-Baptiste Cordonnier. 2020. Group equivariant stand-alone self-attention for vision. arXiv preprint arXiv:2010.00977 (2020)."},{"key":"e_1_3_2_2_29_1","volume-title":"International conference on machine learning. PMLR, 9323--9332","author":"Satorras Victor Garcia","year":"2021","unstructured":"Victor Garcia Satorras , Emiel Hoogeboom , and Max Welling . 2021 . E (n) equivariant graph neural networks . In International conference on machine learning. PMLR, 9323--9332 . Victor Garcia Satorras, Emiel Hoogeboom, and Max Welling. 2021. E (n) equivariant graph neural networks. In International conference on machine learning. PMLR, 9323--9332."},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/2740908.2742839"},{"key":"e_1_3_2_2_31_1","volume-title":"Prototypical networks for few-shot learning. Advances in neural information processing systems","author":"Snell Jake","year":"2017","unstructured":"Jake Snell , Kevin Swersky , and Richard Zemel . 2017. Prototypical networks for few-shot learning. Advances in neural information processing systems , Vol. 30 ( 2017 ). Jake Snell, Kevin Swersky, and Richard Zemel. 2017. Prototypical networks for few-shot learning. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00131"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403230"},{"key":"e_1_3_2_2_34_1","volume-title":"Transductive Linear Probing: A Novel Framework for Few-Shot Node Classification. arXiv preprint arXiv:2212.05606","author":"Tan Zhen","year":"2022","unstructured":"Zhen Tan , Song Wang , Kaize Ding , Jundong Li , and Huan Liu . 2022. Transductive Linear Probing: A Novel Framework for Few-Shot Node Classification. arXiv preprint arXiv:2212.05606 ( 2022 ). Zhen Tan, Song Wang, Kaize Ding, Jundong Li, and Huan Liu. 2022. Transductive Linear Probing: A Novel Framework for Few-Shot Node Classification. arXiv preprint arXiv:2212.05606 (2022)."},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1402008"},{"key":"e_1_3_2_2_36_1","volume-title":"ICLR 2021 Workshop on Geometrical and Topological Representation Learning.","author":"Thakoor Shantanu","year":"2021","unstructured":"Shantanu Thakoor , Corentin Tallec , Mohammad Gheshlaghi Azar , R\u00e9mi Munos , Petar Veli\u010dkovi\u0107 , and Michal Valko . 2021 . Bootstrapped representation learning on graphs . In ICLR 2021 Workshop on Geometrical and Topological Representation Learning. Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, R\u00e9mi Munos, Petar Veli\u010dkovi\u0107, and Michal Valko. 2021. Bootstrapped representation learning on graphs. In ICLR 2021 Workshop on Geometrical and Topological Representation Learning."},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58568-6_16"},{"key":"e_1_3_2_2_38_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 ). 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_2_39_1","unstructured":"Oriol Vinyals Charles Blundell Timothy Lillicrap Daan Wierstra etal 2016. Matching networks for one shot learning. Advances in neural information processing systems Vol. 29 (2016).  Oriol Vinyals Charles Blundell Timothy Lillicrap Daan Wierstra et al. 2016. Matching networks for one shot learning. Advances in neural information processing systems Vol. 29 (2016)."},{"key":"e_1_3_2_2_40_1","volume-title":"Graph few-shot learning with task-specific structures. arXiv preprint arXiv:2210.12130","author":"Wang Song","year":"2022","unstructured":"Song Wang , Chen Chen , and Jundong Li. 2022a. Graph few-shot learning with task-specific structures. arXiv preprint arXiv:2210.12130 ( 2022 ). Song Wang, Chen Chen, and Jundong Li. 2022a. Graph few-shot learning with task-specific structures. arXiv preprint arXiv:2210.12130 (2022)."},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539265"},{"key":"e_1_3_2_2_42_1","volume-title":"Advances in Neural Information Processing Systems","volume":"32","author":"Weiler Maurice","year":"2019","unstructured":"Maurice Weiler and Gabriele Cesa . 2019 . General e (2)-equivariant steerable cnns . Advances in Neural Information Processing Systems , Vol. 32 (2019). Maurice Weiler and Gabriele Cesa. 2019. General e (2)-equivariant steerable cnns. Advances in Neural Information Processing Systems, Vol. 32 (2019)."},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"e_1_3_2_2_44_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_45_1","volume-title":"International conference on machine learning. PMLR, 7134--7143","author":"You Jiaxuan","year":"2019","unstructured":"Jiaxuan You , Rex Ying , and Jure Leskovec . 2019 . Position-aware graph neural networks . In International conference on machine learning. PMLR, 7134--7143 . Jiaxuan You, Rex Ying, and Jure Leskovec. 2019. Position-aware graph neural networks. In International conference on machine learning. PMLR, 7134--7143."},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i3.20226"},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/2502081.2502284"},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btz718"},{"key":"e_1_3_2_2_49_1","volume-title":"Multimodal deep representation learning for protein interaction identification and protein family classification. BMC bioinformatics","author":"Zhang Da","year":"2019","unstructured":"Da Zhang and Mansur Kabuka . 2019. Multimodal deep representation learning for protein interaction identification and protein family classification. BMC bioinformatics , Vol. 20 , 16 ( 2019 ), 1--14. Da Zhang and Mansur Kabuka. 2019. Multimodal deep representation learning for protein interaction identification and protein family classification. BMC bioinformatics, Vol. 20, 16 (2019), 1--14."},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3358106"},{"key":"e_1_3_2_2_51_1","volume-title":"Deep graph contrastive representation learning. arXiv preprint arXiv:2006.04131","author":"Zhu Yanqiao","year":"2020","unstructured":"Yanqiao Zhu , Yichen Xu , Feng Yu , Qiang Liu , Shu Wu , and Liang Wang . 2020. Deep graph contrastive representation learning. arXiv preprint arXiv:2006.04131 ( 2020 ). Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang. 2020. Deep graph contrastive representation learning. arXiv preprint arXiv:2006.04131 (2020)."}],"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\/3580305.3599515","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3580305.3599515","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:37:52Z","timestamp":1750178272000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3580305.3599515"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,4]]},"references-count":51,"alternative-id":["10.1145\/3580305.3599515","10.1145\/3580305"],"URL":"https:\/\/doi.org\/10.1145\/3580305.3599515","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"}}]}}