{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T04:58:51Z","timestamp":1773550731454,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":59,"publisher":"ACM","license":[{"start":{"date-parts":[[2025,7,20]],"date-time":"2025-07-20T00:00:00Z","timestamp":1752969600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62202172"],"award-info":[{"award-number":["62202172"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,7,20]]},"DOI":"10.1145\/3690624.3709251","type":"proceedings-article","created":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T18:44:43Z","timestamp":1743792283000},"page":"2067-2078","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Variational Graph Autoencoder for Heterogeneous Information Networks with Missing and Inaccurate Attributes"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-6195-1665","authenticated-orcid":false,"given":"Yige","family":"Zhao","sequence":"first","affiliation":[{"name":"East China Normal University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-9900-9815","authenticated-orcid":false,"given":"Jianxiang","family":"Yu","sequence":"additional","affiliation":[{"name":"East China Normal University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9179-7032","authenticated-orcid":false,"given":"Yao","family":"Cheng","sequence":"additional","affiliation":[{"name":"East China Normal University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9486-1247","authenticated-orcid":false,"given":"Chengcheng","family":"Yu","sequence":"additional","affiliation":[{"name":"Shanghai Polytechnic University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6857-261X","authenticated-orcid":false,"given":"Yiding","family":"Liu","sequence":"additional","affiliation":[{"name":"Baidu Inc., Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-0142-2483","authenticated-orcid":false,"given":"Xiang","family":"Li","sequence":"additional","affiliation":[{"name":"East China Normal University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9212-1947","authenticated-orcid":false,"given":"Shuaiqiang","family":"Wang","sequence":"additional","affiliation":[{"name":"Baidu Inc., Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2025,7,20]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Graph convolutional matrix completion. arXiv preprint arXiv:1706.02263","author":"van den Berg Rianne","year":"2017","unstructured":"Rianne van den Berg, Thomas N Kipf, and Max Welling. 2017. Graph convolutional matrix completion. arXiv preprint arXiv:1706.02263 (2017)."},{"key":"e_1_3_2_2_2_1","volume-title":"Variational relevance vector machines. arXiv preprint arXiv:1301.3838","author":"Bishop Christopher M","year":"2013","unstructured":"Christopher M Bishop and Michael Tipping. 2013. Variational relevance vector machines. arXiv preprint arXiv:1301.3838 (2013)."},{"key":"e_1_3_2_2_3_1","volume-title":"Relational graph attention networks. arXiv preprint arXiv:1904.05811","author":"Busbridge Dan","year":"2019","unstructured":"Dan Busbridge, Dane Sherburn, Pietro Cavallo, and Nils Y Hammerla. 2019. Relational graph attention networks. arXiv preprint arXiv:1904.05811 (2019)."},{"key":"e_1_3_2_2_4_1","volume-title":"Uncertainty-Aware Robust Learning on Noisy Graphs. arXiv preprint arXiv:2306.08210","author":"Chen Shuyi","year":"2023","unstructured":"Shuyi Chen, Kaize Ding, and Shixiang Zhu. 2023. Uncertainty-Aware Robust Learning on Noisy Graphs. arXiv preprint arXiv:2306.08210 (2023)."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"crossref","unstructured":"Yuxiao Dong Nitesh V Chawla and Ananthram Swami. 2017. metapath2vec: Scalable representation learning for heterogeneous networks. In KDD. 135--144.","DOI":"10.1145\/3097983.3098036"},{"key":"e_1_3_2_2_6_1","first-page":"4861","article-title":"Heterogeneous Network Representation Learning","volume":"20","author":"Dong Yuxiao","year":"2020","unstructured":"Yuxiao Dong, Ziniu Hu, Kuansan Wang, Yizhou Sun, and Jie Tang. 2020. Heterogeneous Network Representation Learning.. In IJCAI, Vol. 20. 4861--4867.","journal-title":"IJCAI"},{"key":"e_1_3_2_2_7_1","volume-title":"Magnn: Metapath aggregated graph neural network for heterogeneous graph embedding. In WebConf. 2331--2341.","author":"Fu Xinyu","year":"2020","unstructured":"Xinyu Fu, Jiani Zhang, Ziqiao Meng, and Irwin King. 2020. Magnn: Metapath aggregated graph neural network for heterogeneous graph embedding. In WebConf. 2331--2341."},{"key":"e_1_3_2_2_8_1","volume-title":"AISTATS. JMLR Workshop and Conference Proceedings, 249--256","author":"Glorot Xavier","year":"2010","unstructured":"Xavier Glorot and Yoshua Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. In AISTATS. JMLR Workshop and Conference Proceedings, 249--256."},{"key":"e_1_3_2_2_9_1","volume-title":"NeurIPS","volume":"30","author":"Hamilton Will","year":"2017","unstructured":"Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. NeurIPS, Vol. 30 (2017)."},{"key":"e_1_3_2_2_10_1","volume-title":"NeurIPS","volume":"32","author":"Hasanzadeh Arman","year":"2019","unstructured":"Arman Hasanzadeh, Ehsan Hajiramezanali, Krishna Narayanan, Nick Duffield, Mingyuan Zhou, and Xiaoning Qian. 2019. Semi-implicit graph variational auto-encoders. NeurIPS, Vol. 32 (2019)."},{"key":"e_1_3_2_2_11_1","volume-title":"Analyzing heterogeneous networks with missing attributes by unsupervised contrastive learning","author":"He Dongxiao","year":"2022","unstructured":"Dongxiao He, Chundong Liang, Cuiying Huo, Zhiyong Feng, Di Jin, Liang Yang, and Weixiong Zhang. 2022. Analyzing heterogeneous networks with missing attributes by unsupervised contrastive learning. IEEE Transactions on Neural Networks and Learning Systems (2022)."},{"key":"e_1_3_2_2_12_1","volume-title":"Graphmae: Self-supervised masked graph autoencoders. In KDD. 594--604.","author":"Hou Zhenyu","year":"2022","unstructured":"Zhenyu Hou, Xiao Liu, Yukuo Cen, Yuxiao Dong, Hongxia Yang, Chunjie Wang, and Jie Tang. 2022. Graphmae: Self-supervised masked graph autoencoders. In KDD. 594--604."},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"crossref","unstructured":"Binbin Hu Yuan Fang and Chuan Shi. 2019. Adversarial learning on heterogeneous information networks. In KDD. 120--129.","DOI":"10.1145\/3292500.3330970"},{"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. 2020b. 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","doi-asserted-by":"crossref","unstructured":"Ziniu Hu Yuxiao Dong Kuansan Wang and Yizhou Sun. 2020a. Heterogeneous graph transformer. In WebConf. 2704--2710.","DOI":"10.1145\/3366423.3380027"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449914"},{"key":"e_1_3_2_2_17_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma Diederik P","year":"2014","unstructured":"Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"e_1_3_2_2_18_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. 2016a. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)."},{"key":"e_1_3_2_2_19_1","volume-title":"Variational graph auto-encoders. arXiv preprint arXiv:1611.07308","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling. 2016b. Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 (2016)."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"crossref","unstructured":"Xiang Li Danhao Ding Ben Kao Yizhou Sun and Nikos Mamoulis. 2021. Leveraging meta-path contexts for classification in heterogeneous information networks. In ICDE. 912--923.","DOI":"10.1109\/ICDE51399.2021.00084"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"crossref","unstructured":"Xiang Li Tiandi Ye Caihua Shan Dongsheng Li and Ming Gao. 2023. SeeGera: Self-supervised Semi-implicit Graph Variational Auto-encoders with Masking. In WebConf. 143--153.","DOI":"10.1145\/3543507.3583245"},{"key":"e_1_3_2_2_22_1","first-page":"5879","article-title":"Graph self-supervised learning: A survey","volume":"35","author":"Liu Yixin","year":"2022","unstructured":"Yixin Liu, Ming Jin, Shirui Pan, Chuan Zhou, Yu Zheng, Feng Xia, and S Yu Philip. 2022. Graph self-supervised learning: A survey. IEEE TKDE, Vol. 35, 6 (2022), 5879--5900.","journal-title":"IEEE TKDE"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33014456"},{"key":"e_1_3_2_2_24_1","volume-title":"Hinormer: Representation learning on heterogeneous information networks with graph transformer. In WebConf. 599--610.","author":"Mao Qiheng","year":"2023","unstructured":"Qiheng Mao, Zemin Liu, Chenghao Liu, and Jianling Sun. 2023. Hinormer: Representation learning on heterogeneous information networks with graph transformer. In WebConf. 599--610."},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"crossref","unstructured":"Zaiqiao Meng Shangsong Liang Hongyan Bao and Xiangliang Zhang. 2019. Co-embedding attributed networks. In WSDM. 393--401.","DOI":"10.1145\/3289600.3291015"},{"key":"e_1_3_2_2_26_1","volume-title":"Adversarially regularized graph autoencoder for graph embedding. arXiv preprint arXiv:1802.04407","author":"Pan Shirui","year":"2018","unstructured":"Shirui Pan, Ruiqi Hu, Guodong Long, Jing Jiang, Lina Yao, and Chengqi Zhang. 2018. Adversarially regularized graph autoencoder for graph embedding. arXiv preprint arXiv:1802.04407 (2018)."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.105861"},{"key":"e_1_3_2_2_28_1","volume-title":"Xiaowen Dong, and Michael M Bronstein.","author":"Rossi Emanuele","year":"2022","unstructured":"Emanuele Rossi, Henry Kenlay, Maria I Gorinova, Benjamin Paul Chamberlain, Xiaowen Dong, and Michael M Bronstein. 2022. On the unreasonable effectiveness of feature propagation in learning on graphs with missing node features. In LOG. PMLR, 11--1."},{"key":"e_1_3_2_2_29_1","volume-title":"Graph attention auto-encoders. arXiv preprint arXiv:1905.10715","author":"Salehi Amin","year":"2019","unstructured":"Amin Salehi and Hasan Davulcu. 2019. Graph attention auto-encoders. arXiv preprint arXiv:1905.10715 (2019)."},{"key":"e_1_3_2_2_30_1","volume-title":"Ivan Titov, and Max Welling.","author":"Schlichtkrull Michael","year":"2018","unstructured":"Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling. 2018. Modeling relational data with graph convolutional networks. In ESWC. Springer, 593--607."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2016.2598561"},{"key":"e_1_3_2_2_32_1","volume-title":"Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization. arXiv preprint arXiv:1908.01000","author":"Sun Fan-Yun","year":"2019","unstructured":"Fan-Yun Sun, Jordan Hoffmann, Vikas Verma, and Jian Tang. 2019b. Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization. arXiv preprint arXiv:1908.01000 (2019)."},{"key":"e_1_3_2_2_33_1","volume-title":"Mining heterogeneous information networks","author":"Sun Yizhou","unstructured":"Yizhou Sun. 2012. Mining heterogeneous information networks. University of Illinois at Urbana-Champaign."},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.14778\/3402707.3402736"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/2500492"},{"key":"e_1_3_2_2_36_1","volume-title":"Rotate: Knowledge graph embedding by relational rotation in complex space. arXiv preprint arXiv:1902.10197","author":"Sun Zhiqing","year":"2019","unstructured":"Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, and Jian Tang. 2019a. Rotate: Knowledge graph embedding by relational rotation in complex space. arXiv preprint arXiv:1902.10197 (2019)."},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2020.11.016"},{"key":"e_1_3_2_2_38_1","volume-title":"Heterogeneous Graph Masked Autoencoders. arXiv preprint arXiv:2208.09957","author":"Tian Yijun","year":"2022","unstructured":"Yijun Tian, Kaiwen Dong, Chunhui Zhang, Chuxu Zhang, and Nitesh V Chawla. 2022. Heterogeneous Graph Masked Autoencoders. arXiv preprint arXiv:2208.09957 (2022)."},{"key":"e_1_3_2_2_39_1","volume-title":"Graph attention networks. arXiv preprint arXiv:1710.10903","author":"Velickovic Petar","year":"2017","unstructured":"Petar Velickovic, 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_40_1","volume-title":"Deep graph infomax. arXiv preprint arXiv:1809.10341","author":"Velickovic Petar","year":"2018","unstructured":"Petar Velickovic, William Fedus, William L Hamilton, Pietro Li\u00f2, Yoshua Bengio, and R Devon Hjelm. 2018. Deep graph infomax. arXiv preprint arXiv:1809.10341 (2018)."},{"key":"e_1_3_2_2_41_1","first-page":"4","article-title":"Deep graph infomax","volume":"2","author":"Velickovic Petar","year":"2019","unstructured":"Petar Velickovic, 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_42_1","doi-asserted-by":"crossref","unstructured":"Daixin Wang Peng Cui and Wenwu Zhu. 2016. Structural deep network embedding. In KDD. 1225--1234.","DOI":"10.1145\/2939672.2939753"},{"key":"e_1_3_2_2_43_1","volume-title":"Hgate: heterogeneous graph attention auto-encoders. TKDE","author":"Wang Wei","year":"2021","unstructured":"Wei Wang, Xiangyu Wei, Xiaoyang Suo, Bin Wang, Hao Wang, Hong-Ning Dai, and Xiangliang Zhang. 2021b. Hgate: heterogeneous graph attention auto-encoders. TKDE (2021)."},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"crossref","unstructured":"Xiao Wang Houye Ji Chuan Shi Bai Wang Yanfang Ye Peng Cui and Philip S Yu. 2019a. Heterogeneous graph attention network. In WebConf. 2022--2032.","DOI":"10.1145\/3308558.3313562"},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"crossref","unstructured":"Xiao Wang Nian Liu Hui Han and Chuan Shi. 2021a. Self-supervised heterogeneous graph neural network with co-contrastive learning. In KDD. 1726--1736.","DOI":"10.1145\/3447548.3467415"},{"key":"e_1_3_2_2_46_1","volume-title":"Graphdefense: Towards robust graph convolutional networks. arXiv preprint arXiv:1911.04429","author":"Wang Xiaoyun","year":"2019","unstructured":"Xiaoyun Wang, Xuanqing Liu, and Cho-Jui Hsieh. 2019b. Graphdefense: Towards robust graph convolutional networks. arXiv preprint arXiv:1911.04429 (2019)."},{"key":"e_1_3_2_2_47_1","volume-title":"Self-supervised learning on graphs: Contrastive, generative, or predictive","author":"Wu Lirong","year":"2021","unstructured":"Lirong Wu, Haitao Lin, Cheng Tan, Zhangyang Gao, and Stan Z Li. 2021. Self-supervised learning on graphs: Contrastive, generative, or predictive. IEEE Transactions on Knowledge and Data Engineering (2021)."},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"crossref","unstructured":"Zhe Xu Boxin Du and Hanghang Tong. 2022. Graph sanitation with application to node classification. In WebConf. 1136--1147.","DOI":"10.1145\/3485447.3512180"},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26283"},{"key":"e_1_3_2_2_50_1","unstructured":"Mingzhang Yin and Mingyuan Zhou. 2018. Semi-implicit variational inference. In ICML. PMLR 5660--5669."},{"key":"e_1_3_2_2_51_1","first-page":"19075","article-title":"Handling missing data with graph representation learning","volume":"33","author":"You Jiaxuan","year":"2020","unstructured":"Jiaxuan You, Xiaobai Ma, Yi Ding, Mykel J Kochenderfer, and Jure Leskovec. 2020b. Handling missing data with graph representation learning. NeurIPS, Vol. 33 (2020), 19075--19087.","journal-title":"NeurIPS"},{"key":"e_1_3_2_2_52_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. 2020a. Graph contrastive learning with augmentations. NeurIPS, Vol. 33 (2020), 5812--5823.","journal-title":"NeurIPS"},{"key":"e_1_3_2_2_53_1","doi-asserted-by":"crossref","unstructured":"Pengyang Yu Chaofan Fu Yanwei Yu Chao Huang Zhongying Zhao and Junyu Dong. 2022. Multiplex heterogeneous graph convolutional network. In KDD. 2377--2387.","DOI":"10.1145\/3534678.3539482"},{"key":"e_1_3_2_2_54_1","volume-title":"NeurIPS","volume":"32","author":"Yun Seongjun","year":"2019","unstructured":"Seongjun Yun, Minbyul Jeong, Raehyun Kim, Jaewoo Kang, and Hyunwoo J Kim. 2019. Graph transformer networks. NeurIPS, Vol. 32 (2019)."},{"key":"e_1_3_2_2_55_1","doi-asserted-by":"crossref","unstructured":"Chuxu Zhang Dongjin Song Chao Huang Ananthram Swami and Nitesh V Chawla. 2019b. Heterogeneous graph neural network. In KDD. 793--803.","DOI":"10.1145\/3292500.3330961"},{"key":"e_1_3_2_2_56_1","volume-title":"Star-gcn: Stacked and reconstructed graph convolutional networks for recommender systems. arXiv preprint arXiv:1905.13129","author":"Zhang Jiani","year":"2019","unstructured":"Jiani Zhang, Xingjian Shi, Shenglin Zhao, and Irwin King. 2019a. Star-gcn: Stacked and reconstructed graph convolutional networks for recommender systems. arXiv preprint arXiv:1905.13129 (2019)."},{"key":"e_1_3_2_2_57_1","volume-title":"AutoAC: Towards Automated Attribute Completion for Heterogeneous Graph Neural Network. arXiv preprint arXiv:2301.03049","author":"Zhu Guanghui","year":"2023","unstructured":"Guanghui Zhu, Zhennan Zhu, Wenjie Wang, Zhuoer Xu, Chunfeng Yuan, and Yihua Huang. 2023. AutoAC: Towards Automated Attribute Completion for Heterogeneous Graph Neural Network. arXiv preprint arXiv:2301.03049 (2023)."},{"key":"e_1_3_2_2_58_1","doi-asserted-by":"crossref","unstructured":"Yanqiao Zhu Yichen Xu Feng Yu Qiang Liu Shu Wu and Liang Wang. 2021. Graph contrastive learning with adaptive augmentation. In WebConf. 2069--2080.","DOI":"10.1145\/3442381.3449802"},{"key":"e_1_3_2_2_59_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394520"}],"event":{"name":"KDD '25: The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Toronto ON Canada","acronym":"KDD '25","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 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3690624.3709251","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3690624.3709251","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,16]],"date-time":"2025-08-16T15:42:26Z","timestamp":1755358946000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3690624.3709251"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,20]]},"references-count":59,"alternative-id":["10.1145\/3690624.3709251","10.1145\/3690624"],"URL":"https:\/\/doi.org\/10.1145\/3690624.3709251","relation":{},"subject":[],"published":{"date-parts":[[2025,7,20]]},"assertion":[{"value":"2025-07-20","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}