{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T17:52:42Z","timestamp":1772301162593,"version":"3.50.1"},"reference-count":56,"publisher":"Association for Computing Machinery (ACM)","issue":"7","license":[{"start":{"date-parts":[[2024,6,19]],"date-time":"2024-06-19T00:00:00Z","timestamp":1718755200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Google Research Scholar Award"},{"name":"UCI-UCLA Collaboration Funding by the Samueli Foundation"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2024,8,31]]},"abstract":"<jats:p>Graphs can facilitate modeling various complex systems such as gene networks and power grids as well as analyzing the underlying relations within them. Learning over graphs has recently attracted increasing attention, particularly graph neural network (GNN)\u2013based solutions, among which graph attention networks (GATs) have become one of the most widely utilized neural network structures for graph-based tasks. Although it is shown that the use of graph structures in learning results in the amplification of algorithmic bias, the influence of the attention design in GATs on algorithmic bias has not been investigated. Motivated by this, the present study first carries out a theoretical analysis in order to demonstrate the sources of algorithmic bias in GAT-based learning for node classification. Then, a novel algorithm, FairGAT, which leverages a fairness-aware attention design, is developed based on the theoretical findings. Experimental results on real-world networks demonstrate that FairGAT improves group fairness measures while also providing comparable utility to the fairness-aware baselines for node classification and link prediction.<\/jats:p>","DOI":"10.1145\/3645096","type":"journal-article","created":{"date-parts":[[2024,2,12]],"date-time":"2024-02-12T11:59:21Z","timestamp":1707739161000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["FairGAT: Fairness-Aware Graph Attention Networks"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8685-2161","authenticated-orcid":false,"given":"O. Deniz","family":"Kose","sequence":"first","affiliation":[{"name":"University of California, Irvine, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7333-893X","authenticated-orcid":false,"given":"Yanning","family":"Shen","sequence":"additional","affiliation":[{"name":"University of California, Irvine, USA"}]}],"member":"320","published-online":{"date-parts":[[2024,6,19]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-72357-6"},{"key":"e_1_3_2_3_2","first-page":"37","volume-title":"Proc. International Conference on World Wide Web (WWW)","author":"Ahmed Amr","year":"2013","unstructured":"Amr Ahmed, Nino Shervashidze, Shravan Narayanamurthy, Vanja Josifovski, and Alexander J. Smola. 2013. Distributed large-scale natural graph factorization. In Proc. International Conference on World Wide Web (WWW). ACM, Rio de Janeiro, Brazil, 37\u201348."},{"key":"e_1_3_2_4_2","first-page":"300","volume-title":"Proc ACM Conference on Knowledge Discovery & Data Mining (KDD)","author":"Berg Rianne van den","year":"2018","unstructured":"Rianne van den Berg, Thomas N. Kipf, and Max Welling. 2018. Graph convolutional matrix completion. In Proc ACM Conference on Knowledge Discovery & Data Mining (KDD). ACM, London, UK, 300\u2013310."},{"key":"e_1_3_2_5_2","first-page":"715","volume-title":"International Conference on Machine Learning","author":"Bose Avishek","year":"2019","unstructured":"Avishek Bose and William Hamilton. 2019. Compositional fairness constraints for graph embeddings. In International Conference on Machine Learning. PMLR, Long Beach, CA, 715\u2013724."},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-86520-7_22"},{"key":"e_1_3_2_7_2","first-page":"1220","volume-title":"International Conference on Machine Learning (ICML)","author":"Buyl Maarten","year":"2020","unstructured":"Maarten Buyl and Tijl De Bie. 2020. DeBayes: A Bayesian method for debiasing network embeddings. In International Conference on Machine Learning (ICML). PMLR, Online, 1220\u20131229."},{"key":"e_1_3_2_8_2","first-page":"891","volume-title":"Proc. ACM International Conference on Information and Knowledge Management (CIKM)","author":"Cao Shaosheng","year":"2015","unstructured":"Shaosheng Cao, Wei Lu, and Qiongkai Xu. 2015. GraRep: Learning graph representations with global structural information. In Proc. ACM International Conference on Information and Knowledge Management (CIKM). ACM, Melbourne, Australia, 891\u2013900."},{"issue":"89","key":"e_1_3_2_9_2","first-page":"1","article-title":"Machine learning on graphs: A model and comprehensive taxonomy","volume":"23","author":"al. Ines Chami et","year":"2022","unstructured":"Ines Chami et al.2022. Machine learning on graphs: A model and comprehensive taxonomy. Journal of Machine Learning Research 23, 89 (2022), 1\u201364.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_10_2","first-page":"2127","volume-title":"Proc. AAAI Conference on Artificial Intelligence","volume":"32","author":"Chen Haochen","year":"2018","unstructured":"Haochen Chen, Bryan Perozzi, Yifan Hu, and Steven Skiena. 2018. HARP: Hierarchical representation learning for networks. In Proc. AAAI Conference on Artificial Intelligence, Vol. 32. AAAI, New Orleans, Louisiana, 2127\u20132134."},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441752"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330958"},{"key":"e_1_3_2_13_2","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1145\/3447548.3467266","volume-title":"Proc. ACM Conference on Knowledge Discovery & Data Mining (SIGKDD)","author":"Dong Yushun","year":"2021","unstructured":"Yushun Dong, Jian Kang, Hanghang Tong, and Jundong Li. 2021. Individual fairness for graph neural networks: A ranking based approach. In Proc. ACM Conference on Knowledge Discovery & Data Mining (SIGKDD). ACM, Singapore, 300\u2013310."},{"key":"e_1_3_2_14_2","first-page":"1259","volume-title":"Proc. ACM Web Conference","author":"al. Yushun Dong et","year":"2022","unstructured":"Yushun Dong et al.2022. EDITS: Modeling and mitigating data bias for graph neural networks. In Proc. ACM Web Conference. ACM, New York, NY, 1259\u20131269."},{"key":"e_1_3_2_15_2","first-page":"214","volume-title":"Proc. Innovations in Theoretical Computer Science Conf.","author":"Dwork Cynthia","year":"2012","unstructured":"Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel. 2012. Fairness through awareness. In Proc. Innovations in Theoretical Computer Science Conf.ACM, New York, NY, 214\u2013226."},{"key":"e_1_3_2_16_2","doi-asserted-by":"crossref","first-page":"7332","DOI":"10.18653\/v1\/2020.emnlp-main.595","volume-title":"Proc. Conference on Empirical Methods in Natural Language Processing (EMNLP)","author":"Fisher Joseph","year":"2020","unstructured":"Joseph Fisher, Arpit Mittal, Dave Palfrey, and Christos Christodoulopoulos. 2020. Debiasing knowledge graph embeddings. In Proc. Conference on Empirical Methods in Natural Language Processing (EMNLP). ACL, online, 7332\u20137345."},{"key":"e_1_3_2_17_2","first-page":"5125","volume-title":"Proc. International Conference on Neural Information Processing Systems (NeurIPS)","author":"Garc\u00eda-Dur\u00e1n Alberto","year":"2017","unstructured":"Alberto Garc\u00eda-Dur\u00e1n and Mathias Niepert. 2017. Learning graph representations with embedding propagation. In Proc. International Conference on Neural Information Processing Systems (NeurIPS). MIT Press, Long Beach, CA, 5125\u20135136."},{"key":"e_1_3_2_18_2","first-page":"249","volume-title":"Proc. International Conference on Artificial Intelligence and Statistics (AISTATS)","author":"Glorot Xavier","year":"2010","unstructured":"Xavier Glorot and Yoshua Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. In Proc. International Conference on Artificial Intelligence and Statistics (AISTATS). PMLR, Sardinia, Italy, 249\u2013256."},{"key":"e_1_3_2_19_2","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1145\/2939672.2939754","volume-title":"Proc. ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD)","author":"Grover Aditya","year":"2016","unstructured":"Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In Proc. ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD). ACM, San Francisco, CA, 855\u2013864."},{"key":"e_1_3_2_20_2","first-page":"11","volume-title":"Proc. Python in Science Conference (SciPy)","author":"Hagberg Aric","year":"2008","unstructured":"Aric Hagberg, Pieter Swart, and Daniel S. Chult. 2008. Exploring network structure, dynamics, and function using NetworkX. In Proc. Python in Science Conference (SciPy). Austin, TX: SciPy, United States, 11\u201315."},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2012.72"},{"key":"e_1_3_2_22_2","first-page":"1025","volume-title":"Proc. International Conference on Neural Information Processing Systems (NeurIPS)","author":"Hamilton William L.","year":"2017","unstructured":"William L. Hamilton, Rex Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Proc. International Conference on Neural Information Processing Systems (NeurIPS). MIT Press, Long Beach, CA, 1025\u20131035."},{"key":"e_1_3_2_23_2","first-page":"3315","article-title":"Equality of opportunity in supervised learning","volume":"29","author":"Hardt Moritz","year":"2016","unstructured":"Moritz Hardt, Eric Price, and Nati Srebro. 2016. Equality of opportunity in supervised learning. Adv. in Neural Information Processing Systems (NeurIPS) 29 (Dec.2016), 3315\u20133323.","journal-title":"Adv. in Neural Information Processing Systems (NeurIPS)"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1177\/0003122417705656"},{"key":"e_1_3_2_25_2","first-page":"4532","volume-title":"Proc. International Joint Conference on Artificial Intelligence, (IJCAI)","author":"Hu Fenyu","year":"2019","unstructured":"Fenyu Hu, Yanqiao Zhu, Shu Wu, Liang Wang, and Tieniu Tan. 2019. Hierarchical graph convolutional networks for semi-supervised node classification. In Proc. International Joint Conference on Artificial Intelligence, (IJCAI). ACM, Macao, China, 4532\u20134539."},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1080\/15377938.2014.984045"},{"key":"e_1_3_2_27_2","first-page":"11963","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"36","author":"Khajehnejad Ahmad","year":"2023","unstructured":"Ahmad Khajehnejad, Moein Khajehnejad, Mahmoudreza Babaei, Krishna P. Gummadi, Adrian Weller, and Baharan Mirzasoleiman. 2023. CrossWalk: Fairness-enhanced node representation learning. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 36. AAAI, Washington DC, 11963\u201311970."},{"key":"e_1_3_2_28_2","article-title":"Adam: A method for stochastic optimization","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).","journal-title":"arXiv preprint arXiv:1412.6980"},{"key":"e_1_3_2_29_2","first-page":"1","volume-title":"Proc. International Conference on Learning Representations (ICLR)","author":"Kipf Thomas N.","year":"2017","unstructured":"Thomas N. Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In Proc. International Conference on Learning Representations (ICLR). ICLR, Toulon, France, 1\u201314."},{"key":"e_1_3_2_30_2","unstructured":"Naveen Kodali Jacob Abernethy James Hays and Zsolt Kira. 2017. On convergence and stability of GANs. (2017). arXiv:1705.07215arXiv:arXiv preprint arXiv:1705.07215"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSIPN.2022.3174953"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/IEEECONF56349.2022.10052007"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3265370"},{"key":"e_1_3_2_34_2","first-page":"1774","volume-title":"International Conference on Artificial Intelligence and Statistics","author":"Laclau Charlotte","year":"2021","unstructured":"Charlotte Laclau, Ievgen Redko, Manvi Choudhary, and Christine Largeron. 2021. All of the fairness for edge prediction with optimal transport. In International Conference on Artificial Intelligence and Statistics. PMLR, online, 1774\u20131782."},{"key":"e_1_3_2_35_2","first-page":"1","volume-title":"Proc. International Conference on Learning Representations (ICLR)","author":"Li Peizhao","year":"2021","unstructured":"Peizhao Li, Yifei Wang, Han Zhao, Pengyu Hong, and Hongfu Liu. 2021. On dyadic fairness: Exploring and mitigating bias in graph connections. In Proc. International Conference on Learning Representations (ICLR). ICLR, Vienna, Austria, 1\u201318."},{"key":"e_1_3_2_36_2","first-page":"1","article-title":"Subgroup generalization and fairness of graph neural networks","volume":"34","author":"Ma Jiaqi","year":"2021","unstructured":"Jiaqi Ma, Junwei Deng, and Qiaozhu Mei. 2021. Subgroup generalization and fairness of graph neural networks. Advances in Neural Information Processing Systems 34 (Dec.2021), 1\u201314.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_37_2","first-page":"3","volume-title":"International Conference on Machine Learning (ICML)","volume":"30","author":"Maas Andrew L.","year":"2013","unstructured":"Andrew L. Maas, Awni Y. Hannun, Andrew Y. Ng, et\u00a0al. 2013. Rectifier nonlinearities improve neural network acoustic models. In International Conference on Machine Learning (ICML), Vol. 30. PMLR, Atlanta, GA, USA, 3."},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5429"},{"key":"e_1_3_2_39_2","first-page":"1105","volume-title":"Proc. ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD)","author":"Ou Mingdong","year":"2016","unstructured":"Mingdong Ou, Peng Cui, Jian Pei, Ziwei Zhang, and Wenwu Zhu. 2016. Asymmetric transitivity preserving graph embedding. In Proc. ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD). ACM, San Francisco, CA, 1105\u20131114."},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2013.01.023"},{"key":"e_1_3_2_41_2","first-page":"1","volume-title":"Proc. International Conference on Neural Information Processing Systems (NeurIPS)","volume":"32","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et\u00a0al. 2019. PyTorch: An imperative style, high-performance deep learning library. In Proc. International Conference on Neural Information Processing Systems (NeurIPS), Vol. 32. MIT Press, Vancouver, BC, Canada, 1\u201312."},{"key":"e_1_3_2_42_2","doi-asserted-by":"crossref","first-page":"560","DOI":"10.1145\/1401890.1401959","volume-title":"Proc. International Conference on Knowledge Discovery and Data Mining (SIGKDD)","author":"Pedreshi Dino","year":"2008","unstructured":"Dino Pedreshi, Salvatore Ruggieri, and Franco Turini. 2008. Discrimination-aware data mining. In Proc. International Conference on Knowledge Discovery and Data Mining (SIGKDD). ACM, Las Vegas, NV, USA, 560\u2013568."},{"key":"e_1_3_2_43_2","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1145\/2623330.2623732","volume-title":"Proc. ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD)","author":"Perozzi Bryan","year":"2014","unstructured":"Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014. DeepWalk: Online learning of social representations. In Proc. ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD). ACM, New York, NY, 701\u2013710."},{"key":"e_1_3_2_44_2","first-page":"3289","volume-title":"Proc. International Joint Conference on Artificial Intelligence (IJCAI)","author":"Rahman Tahleen A.","year":"2019","unstructured":"Tahleen A. Rahman, Bartlomiej Surma, Michael Backes, and Yang Zhang. 2019. Fairwalk: Towards fair graph embedding. In Proc. International Joint Conference on Artificial Intelligence (IJCAI). IJCAI, Macao, China, 3289\u20133295."},{"key":"e_1_3_2_45_2","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1145\/1772690.1772778","volume-title":"Proc. International Conference on World Wide Web","author":"Sala Alessandra","year":"2010","unstructured":"Alessandra Sala, Lili Cao, Christo Wilson, Robert Zablit, Haitao Zheng, and Ben Y. Zhao. 2010. Measurement-calibrated graph models for social network experiments. In Proc. International Conference on World Wide Web. 861\u2013870."},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2021.3133818"},{"key":"e_1_3_2_47_2","first-page":"1067","volume-title":"Proc. International Conference on World Wide Web (WWW)","author":"Tang Jian","year":"2015","unstructured":"Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. 2015. LINE: Large-scale information network embedding. In Proc. International Conference on World Wide Web (WWW). ACM, Firenze, Italy, 1067\u20131077."},{"key":"e_1_3_2_48_2","first-page":"1","volume-title":"International Conference on Learning Representations (ICLR)","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 International Conference on Learning Representations (ICLR). ICLR, Vancouver, BC, Canada, 1\u201312."},{"key":"e_1_3_2_49_2","first-page":"1","volume-title":"Proc. International Conference on Learning Representations (ICLR)","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. In Proc. International Conference on Learning Representations (ICLR). ICLR, New Orleans, Louisiana, 1\u201317."},{"key":"e_1_3_2_50_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.0020089"},{"key":"e_1_3_2_51_2","doi-asserted-by":"crossref","first-page":"1064","DOI":"10.1145\/3292500.3330931","volume-title":"Proc. International Conference on Knowledge Discovery & Data Mining (SIGKDD)","author":"Wang Hao","year":"2019","unstructured":"Hao Wang, Tong Xu, Qi Liu, Defu Lian, Enhong Chen, Dongfang Du, Han Wu, and Wen Su. 2019. MCNE: An end-to-end framework for learning multiple conditional network representations of social network. In Proc. International Conference on Knowledge Discovery & Data Mining (SIGKDD). ACM, Anchorage, AK, 1064\u20131072."},{"key":"e_1_3_2_52_2","first-page":"6861","volume-title":"Proc. International Conference on Machine Learning (ICML)","author":"Wu Felix","year":"2019","unstructured":"Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger. 2019. Simplifying graph convolutional networks. In Proc. International Conference on Machine Learning (ICML). PMLR, Long Beach, CA, 6861\u20136871."},{"key":"e_1_3_2_53_2","article-title":"Spectral norm regularization for improving the generalizability of deep learning","author":"Yoshida Yuichi","year":"2017","unstructured":"Yuichi Yoshida and Takeru Miyato. 2017. Spectral norm regularization for improving the generalizability of deep learning. arXiv preprint arXiv:1705.10941 (2017).","journal-title":"arXiv preprint arXiv:1705.10941"},{"key":"e_1_3_2_54_2","first-page":"877","volume-title":"Proc. International AAAI Conference on Web and Social Media","volume":"15","author":"Zeng Ziqian","year":"2021","unstructured":"Ziqian Zeng, Rashidul Islam, Kamrun Naher Keya, James Foulds, Yangqiu Song, and Shimei Pan. 2021. Fair representation learning for heterogeneous information networks. In Proc. International AAAI Conference on Web and Social Media, Vol. 15. AAAI, Vancouver, Canada, 877\u2013887."},{"key":"e_1_3_2_55_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiopen.2021.01.001"},{"key":"e_1_3_2_56_2","first-page":"1","volume-title":"Proc. International Conference on Machine Learning (ICML) Workshop on Graph Representation Learning and Beyond","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. In Proc. International Conference on Machine Learning (ICML) Workshop on Graph Representation Learning and Beyond. PMLR, online, 1\u201317."},{"key":"e_1_3_2_57_2","first-page":"2069","volume-title":"Proc. Web Conference (WWW)","author":"Zhu Yanqiao","year":"2021","unstructured":"Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang. 2021. Graph contrastive learning with adaptive augmentation. In Proc. Web Conference (WWW). ACM, Ljubljana, Slovenia, 2069\u20132080."}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3645096","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3645096","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:03:27Z","timestamp":1750291407000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3645096"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,19]]},"references-count":56,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2024,8,31]]}},"alternative-id":["10.1145\/3645096"],"URL":"https:\/\/doi.org\/10.1145\/3645096","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"value":"1556-4681","type":"print"},{"value":"1556-472X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,19]]},"assertion":[{"value":"2023-09-15","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-01-26","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-06-19","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}