{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T10:58:41Z","timestamp":1756897121183,"version":"3.41.0"},"reference-count":67,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2025,5,9]],"date-time":"2025-05-09T00:00:00Z","timestamp":1746748800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2025,5,31]]},"abstract":"<jats:p>Self-supervised learning on graph neural networks is receiving increasing attention due to the difficulty of obtaining graph labels in many real applications. Graph contrastive learning (GCL), a recently popular method for self-supervised learning on graphs, has achieved great success in many tasks. The key to the effectiveness of GCL is the construction of suitable contrasting pairs to capture important attributes of the data through the data augmentation modules. However, most of the existing approaches fail to fully consider both data diversity and the semantic consistency when conducting data augmentation. To fill this gap, we propose an augmentation diversity and semantic consistency balanced graph contrastive learning model (ADSCB for short), which enhances the representation ability of the CL model through richer contrasting objectives. In particular, we first introduce a semantic consistency module to extract the subgraph from the original graph through optimizing a carefully designed semantic consistency loss. Then, we introduce an augmentation diversity module and perform data augmentation and cross-scale mix-up operations on the original graph and the extracted semantic preserved subgraph to generate more diverse contrasting pairs. With the above two modules, our model ultimately achieves two contrasting objectives: diversity contrasting and semantic contrasting. The tradeoff between these two contrasting objectives allows our model to benefit from both the augmentation diversity and the semantic consistency. We evaluate ADSCB for graph classification in unsupervised, semi-supervised, and transfer learning settings using standard graph contrastive learning benchmarks. The results demonstrate the superiority of our method against several state-of-the-art baselines.<\/jats:p>","DOI":"10.1145\/3728646","type":"journal-article","created":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T14:02:21Z","timestamp":1744207341000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Have Our Cake and Eat It: Augmentation Diversity and Semantic Consistency Balanced Graph Contrastive Learning"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-7631-9375","authenticated-orcid":false,"given":"Hao","family":"Yan","sequence":"first","affiliation":[{"name":"Central South University, Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3615-4859","authenticated-orcid":false,"given":"Senzhang","family":"Wang","sequence":"additional","affiliation":[{"name":"Central South University, Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8179-7503","authenticated-orcid":false,"given":"Chaozhuo","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6714-3476","authenticated-orcid":false,"given":"Jun","family":"Yin","sequence":"additional","affiliation":[{"name":"Central South University, Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3491-5968","authenticated-orcid":false,"given":"Philip S.","family":"Yu","sequence":"additional","affiliation":[{"name":"University of Illinois at Chicago, Chicago, Illinois, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1516-0480","authenticated-orcid":false,"given":"Jianxin","family":"Wang","sequence":"additional","affiliation":[{"name":"Central South University, Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,5,9]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-93037-4_14"},{"key":"e_1_3_1_3_2","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Belghazi Mohamed Ishmael","year":"2018","unstructured":"Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeshwar, Sherjil Ozair, Yoshua Bengio, Aaron Courville, and Devon Hjelm. 2018. Mutual information neural estimation. In Proceedings of the International Conference on Machine Learning."},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bti1007"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/1961189.1961199"},{"key":"e_1_3_1_6_2","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Chen Ting","year":"2020","unstructured":"Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. Hinton. 2020. A simple framework for contrastive learning of visual booktitle. In Proceedings of the International Conference on Machine Learning."},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1021\/jm00106a046"},{"key":"e_1_3_1_8_2","volume-title":"Proceedings of the International Conference on Machine Learning 2019 Workshop on Learning and Reasoning with Graph-Structured Data","author":"Diehl Frederik","year":"2019","unstructured":"Frederik Diehl, Thomas Brunner, Michael Truong Le, and Alois Knoll. 2019. Towards graph pooling by edge contraction. In Proceedings of the International Conference on Machine Learning 2019 Workshop on Learning and Reasoning with Graph-Structured Data."},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/199"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512183"},{"key":"e_1_3_1_11_2","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Gao Hongyang","year":"2019","unstructured":"Hongyang Gao and Shuiwang Ji. 2019. Graph u-nets. In Proceedings of the International Conference on Machine Learning."},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_3_1_13_2","volume-title":"Proceedings of the Neural Information Processing Systems","author":"Hamilton William L.","year":"2017","unstructured":"William L. Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Proceedings of the Neural Information Processing Systems."},{"key":"e_1_3_1_14_2","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Hassani Kaveh","year":"2020","unstructured":"Kaveh Hassani and Amir Hosein Khasahmadi. 2020. Contrastive multi-view representation learning on graphs. In Proceedings of the International Conference on Machine Learning."},{"key":"e_1_3_1_15_2","unstructured":"Kaveh Hassani and Amir Hosein Khasahmadi. 2022. Learning graph augmentations to learn graph representations. arXiv:2201.09830. Retrieved from https:\/\/arxiv.org\/abs\/2201.09830"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/17.1.107"},{"key":"e_1_3_1_17_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Hjelm R. Devon","year":"2018","unstructured":"R. Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio. 2018. Learning deep representations by mutual information estimation and maximization. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-05392-9"},{"key":"e_1_3_1_19_2","article-title":"GraphMAE: Self-Supervised Masked Graph Autoencoders","author":"Hou Zhenyu","year":"2022","unstructured":"Zhenyu Hou, Xiao Liu, Yukuo Cen, Yuxiao Dong, Hongxia Yang, Chunjie Wang, Jie Tang. 2022. GraphMAE: Self-Supervised Masked Graph Autoencoders. arXiv:2205.10803. Retrieved from https:\/\/arxiv.org\/abs\/2205.10803","journal-title":"arXiv:2205.10803"},{"key":"e_1_3_1_20_2","volume-title":"Proceedings of the Neural Information Processing Systems","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. In Proceedings of the Neural Information Processing Systems."},{"key":"e_1_3_1_21_2","unstructured":"Weihua Hu Bowen Liu Joseph Gomes Marinka Zitnik Percy Liang Vijay Pande and Jure Leskovec. 2019. Strategies for pre-training graph neural networks. arXiv:1905.12265. Retrieved from https:\/\/arxiv.org\/abs\/1905.12265"},{"key":"e_1_3_1_22_2","doi-asserted-by":"crossref","unstructured":"Ming Jin Yizhen Zheng Yuan-Fang Li Chen Gong Chuan Zhou and Shirui Pan. 2021. Multi-scale contrastive Siamese networks for self-supervised graph representation learning. arXiv:2105.05682. Retrieved from https:\/\/arxiv.org\/abs\/2105.05682","DOI":"10.24963\/ijcai.2021\/204"},{"key":"e_1_3_1_23_2","volume-title":"Proceedings of the Neural Information Processing Systems","author":"Kipf Thomas N.","year":"2016","unstructured":"Thomas N. Kipf and Max Welling. 2016. Variational graph auto-encoders. In Proceedings of the Neural Information Processing Systems."},{"key":"e_1_3_1_24_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Kipf Thomas N.","year":"2017","unstructured":"Thomas N. Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_1_25_2","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Kriege Nils M.","year":"2012","unstructured":"Nils M. Kriege and Petra Mutzel. 2012. Subgraph matching kernels for attributed graphs. In Proceedings of the International Conference on Machine Learning."},{"key":"e_1_3_1_26_2","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Lee Junhyun","year":"2019","unstructured":"Junhyun Lee, Inyeop Lee, and Jaewoo Kang. 2019. Self-attention graph pooling. In Proceedings of the International Conference on Machine Learning."},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-55753-3_11"},{"key":"e_1_3_1_28_2","volume-title":"Proceedings of the Neural Information Processing Systems","author":"Li Haoyang","year":"2021","unstructured":"Haoyang Li, Xin Wang, Ziwei Zhang, Zehuan Yuan, Hang Li, and Wenwu Zhu. 2021. Disentangled contrastive learning on graphs. In Proceedings of the Neural Information Processing Systems."},{"key":"e_1_3_1_29_2","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Li Rui","year":"2022","unstructured":"Rui Li, Jianan Zhao, Chaozhuo Li, Di He, Yiqi Wang, Yuming Liu, Hao Sun, Senzhang Wang, Weiwei Deng, Yanming Shen. 2022. HousE: Knowledge graph embedding with householder parameterization. In Proceedings of the International Conference on Machine Learning."},{"key":"e_1_3_1_30_2","volume-title":"Proceedings of the Neural Information Processing Systems","author":"Lin Minhua","year":"2024","unstructured":"Minhua Lin, Teng Xiao, Enyan Dai, Xiang Zhang, and Suhang Wang. 2024. Certifiably robust graph contrastive learning. In Proceedings of the Neural Information Processing Systems."},{"key":"e_1_3_1_31_2","first-page":"5879","article-title":"Graph self-supervised learning: A survey","author":"Liu Yixin","year":"2022","unstructured":"Yixin Liu, Ming Jin, Shirui Pan, Chuan Zhou, Yu Zheng, Feng Xia, and Philip Yu. 2022. Graph self-supervised learning: A survey. IEEE Transactions on Knowledge and Data Engineering 35, 6 (2022), 5879\u20135900.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1039\/C8SC00148K"},{"key":"e_1_3_1_33_2","volume-title":"Proceedings of the Neural Information Processing Systems","author":"Mesquita Diego","year":"2020","unstructured":"Diego Mesquita, Amauri H. de Souza, and Samuel Kaski. 2020. Rethinking pooling in graph neural networks. In Proceedings of the Neural Information Processing Systems."},{"key":"e_1_3_1_34_2","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Miao Siqi","year":"2022","unstructured":"Siqi Miao, Mia Liu, and Pan Li. 2022. Interpretable and generalizable graph learning via stochastic attention mechanism. In Proceedings of the International Conference on Machine Learning."},{"key":"e_1_3_1_35_2","volume-title":"Proceedings of the International Conference on Machine Learning Workshop on Graph Representation Learning and Beyond","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. In Proceedings of the International Conference on Machine Learning Workshop on Graph Representation Learning and Beyond."},{"key":"e_1_3_1_36_2","volume-title":"Proceedings of the MLG","author":"Narayanan Annamalai","year":"2017","unstructured":"Annamalai Narayanan, Mahinthan Chandramohan, Rajasekar Venkatesan, Lihui Chen, Yang Liu, and Shantanu Jaiswal. 2017. graph2vec: Learning distributed representations of graphs. In Proceedings of the MLG."},{"key":"e_1_3_1_37_2","unstructured":"Aaron van den Oord Yazhe Li and Oriol Vinyals. 2018. Representation learning with contrastive predictive coding. arXiv:1807.03748. Retrieved from https:\/\/arxiv.org\/abs\/1807.03748"},{"key":"e_1_3_1_38_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3463074"},{"key":"e_1_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098061"},{"key":"e_1_3_1_40_2","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v29i3.2157"},{"issue":"77","key":"e_1_3_1_41_2","first-page":"2539","article-title":"Weisfeiler-lehman graph kernels","volume":"12","author":"Shervashidze Nino","year":"2011","unstructured":"Nino Shervashidze, Pascal Schweitzer, Erik Jan Van Leeuwen, Kurt Mehlhorn, and Karsten M. Borgwardt. 2011. Weisfeiler-lehman graph kernels. Journal of Machine Learning Research 12, 77 (2011), 2539\u20132561.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_1_42_2","first-page":"488","volume-title":"Artificial Intelligence and Statistics","author":"Shervashidze Nino","year":"2009","unstructured":"Nino Shervashidze, S. V. N. Vishwanathan, Tobias Petri, Kurt Mehlhorn, and Karsten Borgwardt. 2009. Efficient graphlet kernels for large graph comparison. In Artificial Intelligence and Statistics, 488\u2013495."},{"key":"e_1_3_1_43_2","volume-title":"Proceedings of the Neural Information Processing Systems","author":"Kihyuk Sohn","year":"2016","unstructured":"Kihyuk Sohn. 2016. Improved deep metric learning with multi-class N-pair loss objective. In Proceedings of the Neural Information Processing Systems."},{"key":"e_1_3_1_44_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Sun Fan-Yun","year":"2019","unstructured":"Fan-Yun Sun, Jordan Hoffman, Vikas Verma, and Jian Tang. 2019. InfoGraph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_1_45_2","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467186"},{"key":"e_1_3_1_46_2","volume-title":"Proceedings of the Neural Information Processing Systems","author":"Suresh Susheel","year":"2021","unstructured":"Susheel Suresh, Pan Li, Cong Hao, and Jennifer Neville. 2021. Adversarial graph augmentation to improve graph contrastive learning. In Proceedings of the Neural Information Processing Systems."},{"key":"e_1_3_1_47_2","unstructured":"Shantanu Thakoor Corentin Tallec Mohammad Gheshlaghi Azar R\u00e9mi Munos Petar Veli\u010dkovi\u0107 and Michal Valko. 2021. Bootstrapped representation learning on graphs. arXiv:2102.06514. Retrieved from https:\/\/arxiv.org\/abs\/2102.06514"},{"key":"e_1_3_1_48_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Veli\u010dkovi\u0107 Petar","year":"2018","unstructured":"Petar Veli\u010dkovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li\u00f2, and Yoshua Bengio. 2018. Graph attention networks. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_1_49_2","volume-title":"Proceedings of the International Conference on Learning Representations","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. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_1_50_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-007-0103-5"},{"key":"e_1_3_1_51_2","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611977653.ch19"},{"key":"e_1_3_1_52_2","volume-title":"Proceedings of the Neural Information Processing Systems","author":"Wang Senzhang","year":"2023","unstructured":"Senzhang Wang, Jun Yin, Chaozhuo Li, Xing Xie, and Jianxin Wang. 2023. V-InFoR: A robust graph neural networks explainer for structurally corrupted graphs. In Proceedings of the Neural Information Processing Systems."},{"key":"e_1_3_1_53_2","first-page":"4216","article-title":"Self-supervised learning on graphs: Contrastive, generative, or predictive","author":"Wu Lirong","year":"2021","unstructured":"Lirong Wu, Haitao Lin, Zhangyang Gao, Cheng Tan, and Stan.Z.Li. 2021. Self-supervised learning on graphs: Contrastive, generative, or predictive. IEEE Transactions on Knowledge and Data Engineering 35, 4 (2021), 4216\u20134235.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_1_54_2","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512156"},{"key":"e_1_3_1_55_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Xu Keyulu","year":"2019","unstructured":"Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019. How powerful are graph neural networks? In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_1_56_2","volume-title":"Proceedings of the Neural Information Processing Systems","author":"Yan Hao","year":"2023","unstructured":"Hao Yan, Chaozhuo Li, Ruosong Long, Chao Yan, Jianan Zhao, Wenwen Zhuang, Jun Yin, Peiyan Zhang, Weihao Han, Hao Sun, et al. 2023. A comprehensive study on text-attributed graphs: Benchmarking and rethinking. In Proceedings of the Neural Information Processing Systems."},{"key":"e_1_3_1_57_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-43415-0_41"},{"key":"e_1_3_1_58_2","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783417"},{"key":"e_1_3_1_59_2","volume-title":"Proceedings of the Neural Information Processing Systems","author":"Yin Jun","year":"2023","unstructured":"Jun Yin, Chaozhuo Li, Hao Yan, Jianxun Lian, and Senzhang Wang. 2023. Train once and explain everywhere: Pre-training interpretable graph neural networks. In Proceedings of the Neural Information Processing Systems."},{"key":"e_1_3_1_60_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20871"},{"key":"e_1_3_1_61_2","volume-title":"Proceedings of the Neural Information Processing Systems","author":"Ying Zhitao","year":"2018","unstructured":"Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton, and Jure Leskovec. 2018. Hierarchical graph representation learning with differentiable pooling. In Proceedings of the Neural Information Processing Systems."},{"key":"e_1_3_1_62_2","volume-title":"Proceedings of the International Conference on Machine Learning","author":"You Yuning","year":"2021","unstructured":"Yuning You, Tianlong Chen, Yang Shen, and Zhangyang Wang. 2021. Graph contrastive learning automated. In Proceedings of the International Conference on Machine Learning."},{"key":"e_1_3_1_63_2","volume-title":"Proceedings of the Neural Information Processing Systems","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. In Proceedings of the Neural Information Processing Systems."},{"key":"e_1_3_1_64_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17293"},{"key":"e_1_3_1_65_2","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Zhang Shichang","year":"2021","unstructured":"Shichang Zhang, Ziniu Hu, Arjun Subramonian, and Yizhou Sun. 2021. Motif-driven contrastive learning of graph representations. In Proceedings of the AAAI Conference on Artificial Intelligence."},{"key":"e_1_3_1_66_2","unstructured":"Jianan Zhao Meng Qu Chaozhuo Li Hao Yan Qian Liu Rui Li Xing Xie and Jian Tang. 2022. Learning on large-scale text-attributed graphs via variational inference. arXiv:2210.14709. Retrieved from https:\/\/arxiv.org\/abs\/2210.14709"},{"key":"e_1_3_1_67_2","unstructured":"Yanqiao Zhu Yichen Xu Feng Yu Qiang Liu Shu Wu and Liang Wang. 2020. Deep graph contrastive representation learning. arXiv:2006.04131. Retrieved from https:\/\/arxiv.org\/abs\/2006.04131"},{"key":"e_1_3_1_68_2","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449802"}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3728646","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3728646","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:18:36Z","timestamp":1750295916000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3728646"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,9]]},"references-count":67,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,5,31]]}},"alternative-id":["10.1145\/3728646"],"URL":"https:\/\/doi.org\/10.1145\/3728646","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"type":"print","value":"1556-4681"},{"type":"electronic","value":"1556-472X"}],"subject":[],"published":{"date-parts":[[2025,5,9]]},"assertion":[{"value":"2023-09-17","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-03-24","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-05-09","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}