{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T14:56:20Z","timestamp":1777733780281,"version":"3.51.4"},"reference-count":117,"publisher":"Association for Computing Machinery (ACM)","issue":"6","license":[{"start":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T00:00:00Z","timestamp":1712880000000},"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":[[2024,7,31]]},"abstract":"<jats:p>Graph Neural Networks (GNNs) have become increasingly important due to their representational power and state-of-the-art predictive performance on many fundamental learning tasks. Despite this success, GNNs suffer from fairness issues that arise as a result of the underlying graph data and the fundamental aggregation mechanism that lies at the heart of the large class of GNN models. In this article, we examine and categorize fairness techniques for improving the fairness of GNNs. We categorize these techniques by whether they focus on improving fairness in the pre-processing, in-processing (during training), or post-processing phases. We discuss how such techniques can be used together whenever appropriate and highlight the advantages and intuition as well. We also introduce an intuitive taxonomy for fairness evaluation metrics, including graph-level fairness, neighborhood-level fairness, embedding-level fairness, and prediction-level fairness metrics. In addition, graph datasets that are useful for benchmarking the fairness of GNN models are summarized succinctly. Finally, we highlight key open problems and challenges that remain to be addressed.<\/jats:p>","DOI":"10.1145\/3649142","type":"journal-article","created":{"date-parts":[[2024,2,24]],"date-time":"2024-02-24T09:17:20Z","timestamp":1708766240000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":19,"title":["Fairness-Aware Graph Neural Networks: A Survey"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0463-2414","authenticated-orcid":false,"given":"April","family":"Chen","sequence":"first","affiliation":[{"name":"Harvard University, Cambridge, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9758-0635","authenticated-orcid":false,"given":"Ryan A.","family":"Rossi","sequence":"additional","affiliation":[{"name":"Adobe Research, San Jose, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3344-2361","authenticated-orcid":false,"given":"Namyong","family":"Park","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, Pittsburgh, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1874-8992","authenticated-orcid":false,"given":"Puja","family":"Trivedi","sequence":"additional","affiliation":[{"name":"University of Michigan, Ann Arbor, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6908-508X","authenticated-orcid":false,"given":"Yu","family":"Wang","sequence":"additional","affiliation":[{"name":"Vanderbilt University, Nashville, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5991-2050","authenticated-orcid":false,"given":"Tong","family":"Yu","sequence":"additional","affiliation":[{"name":"Adobe Research, San Jose, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3580-5290","authenticated-orcid":false,"given":"Sungchul","family":"Kim","sequence":"additional","affiliation":[{"name":"Adobe Research, San Jose, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1119-1346","authenticated-orcid":false,"given":"Franck","family":"Dernoncourt","sequence":"additional","affiliation":[{"name":"Adobe Research, Seattle, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7913-4962","authenticated-orcid":false,"given":"Nesreen K.","family":"Ahmed","sequence":"additional","affiliation":[{"name":"Intel Labs, Santa Clara, United States"}]}],"member":"320","published-online":{"date-parts":[[2024,4,12]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"e_1_3_1_3_2","article-title":"On the interaction between node fairness and edge privacy in graph neural networks","author":"Zhang He","year":"2023","unstructured":"He Zhang, Xingliang Yuan, Quoc Viet Hung Nguyen, and Shirui Pan. 2023. On the interaction between node fairness and edge privacy in graph neural networks. arXiv preprint arXiv:2301.12951 (2023).","journal-title":"arXiv preprint arXiv:2301.12951"},{"key":"e_1_3_1_4_2","article-title":"Graph learning with localized neighborhood fairness","author":"Chen April","year":"2022","unstructured":"April Chen, Ryan Rossi, Nedim Lipka, Jane Hoffswell, Gromit Chan, Shunan Guo, Eunyee Koh, Sungchul Kim, and Nesreen K. Ahmed. 2022. Graph learning with localized neighborhood fairness. arXiv preprint arXiv:2212.12040 (2022).","journal-title":"arXiv preprint arXiv:2212.12040"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM54844.2022.00117"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539346"},{"key":"e_1_3_1_7_2","article-title":"GFairHint: Improving individual fairness for graph neural networks via fairness hint","author":"Xu Paiheng","year":"2023","unstructured":"Paiheng Xu, Yuhang Zhou, Bang An, Wei Ai, and Furong Huang. 2023. GFairHint: Improving individual fairness for graph neural networks via fairness hint. arXiv preprint arXiv:2305.15622 (2023).","journal-title":"arXiv preprint arXiv:2305.15622"},{"key":"e_1_3_1_8_2","article-title":"Analyzing the effect of sampling in GNNs on individual fairness","author":"Salganik Rebecca","year":"2022","unstructured":"Rebecca Salganik, Fernando Diaz, and Golnoosh Farnadi. 2022. Analyzing the effect of sampling in GNNs on individual fairness. arXiv preprint arXiv:2209.03904 (2022).","journal-title":"arXiv preprint arXiv:2209.03904"},{"key":"e_1_3_1_9_2","article-title":"FairNorm: Fair and fast graph neural network training","author":"Kose O. Deniz","year":"2022","unstructured":"O. Deniz Kose and Yanning Shen. 2022. FairNorm: Fair and fast graph neural network training. arXiv preprint arXiv:2205.09977 (2022).","journal-title":"arXiv preprint arXiv:2205.09977"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20808"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.23919\/EUSIPCO55093.2022.9909546"},{"key":"e_1_3_1_12_2","article-title":"Fair node representation learning via adaptive data augmentation","author":"Kose O. Deniz","year":"2022","unstructured":"O. Deniz Kose and Yanning Shen. 2022. Fair node representation learning via adaptive data augmentation. arXiv preprint arXiv:2201.08549 (2022).","journal-title":"arXiv preprint arXiv:2201.08549"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583207"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611977653.ch18"},{"key":"e_1_3_1_15_2","article-title":"Fast&Fair: Training acceleration and bias mitigation for GNNs","author":"Kose Oyku Deniz","year":"2022","unstructured":"Oyku Deniz Kose and Yanning Shen. 2022. Fast&Fair: Training acceleration and bias mitigation for GNNs. Transactions on Machine Learning Research (2022).","journal-title":"Transactions on Machine Learning Research"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-30678-5_64"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1137\/S003614450342480"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1038\/30918"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/BigData55660.2022.10020318"},{"key":"e_1_3_1_20_2","first-page":"1048","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 (2021), 1048\u20131061.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_21_2","article-title":"On graph neural network fairness in the presence of heterophilous neighborhoods","author":"Loveland Donald","year":"2022","unstructured":"Donald Loveland, Jiong Zhu, Mark Heimann, Ben Fish, Michael T. Schaub, and Danai Koutra. 2022. On graph neural network fairness in the presence of heterophilous neighborhoods. arXiv preprint arXiv:2207.04376 (2022).","journal-title":"arXiv preprint arXiv:2207.04376"},{"key":"e_1_3_1_22_2","doi-asserted-by":"crossref","first-page":"2114","DOI":"10.1007\/978-3-030-72357-6","volume-title":"Uncertainty in Artificial Intelligence","author":"Agarwal Chirag","year":"2021","unstructured":"Chirag Agarwal, Himabindu Lakkaraju, and Marinka Zitnik. 2021. Towards a unified framework for fair and stable graph representation learning. In Uncertainty in Artificial Intelligence. PMLR, 2114\u20132124."},{"key":"e_1_3_1_23_2","article-title":"Fairness-aware message passing for graph neural networks","author":"Zhu Huaisheng","year":"2023","unstructured":"Huaisheng Zhu, Guoji Fu, Zhimeng Guo, Zhiwei Zhang, Teng Xiao, and Suhang Wang. 2023. Fairness-aware message passing for graph neural networks. arXiv preprint arXiv:2306.11132 (2023).","journal-title":"arXiv preprint arXiv:2306.11132"},{"key":"e_1_3_1_24_2","first-page":"1220","volume-title":"Proceedings of the 37th International Conference on Machine Learning (Proceedings of Machine Learning Research)","volume":"119","author":"Buyl Maarten","year":"2020","unstructured":"Maarten Buyl and Tijl De Bie. 2020. DeBayes: A Bayesian method for debiasing network embeddings. In Proceedings of the 37th International Conference on Machine Learning (Proceedings of Machine Learning Research), Hal Daum\u00e9 III and Aarti Singh (Eds.), Vol. 119. PMLR, 1220\u20131229."},{"key":"e_1_3_1_25_2","volume-title":"International Conference on Learning Representations","author":"Li Peizhao","year":"2020","unstructured":"Peizhao Li, Yifei Wang, Han Zhao, Pengyu Hong, and Hongfu Liu. 2020. On dyadic fairness: Exploring and mitigating bias in graph connections. In International Conference on Learning Representations."},{"key":"e_1_3_1_26_2","doi-asserted-by":"crossref","first-page":"1194","DOI":"10.1145\/3366423.3380196","volume-title":"WWW","author":"Patro Gourab K.","year":"2020","unstructured":"Gourab K. Patro, Arpita Biswas, Niloy Ganguly, Krishna P. Gummadi, and Abhijnan Chakraborty. 2020. FairRec: Two-sided fairness for personalized recommendations in two-sided platforms. In WWW. 1194\u20131204."},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2021.3133818"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","unstructured":"Tahleen Rahman Bartlomiej Surma Michael Backes and Yang Zhang. 2019. Fairwalk: Towards fair graph embedding. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence Main Track. 3289\u20133295. 10.24963\/ijcai.2019\/456","DOI":"10.24963\/ijcai.2019\/456"},{"key":"e_1_3_1_29_2","article-title":"Fairness in graph mining: A survey","author":"Dong Yushun","year":"2022","unstructured":"Yushun Dong, Jing Ma, Chen Chen, and Jundong Li. 2022. Fairness in graph mining: A survey. arXiv preprint arXiv:2204.09888 (2022).","journal-title":"arXiv preprint arXiv:2204.09888"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3265598"},{"key":"e_1_3_1_31_2","article-title":"Fairness amidst non-iid graph data: A literature review","author":"Zhang Wenbin","year":"2022","unstructured":"Wenbin Zhang, Jeremy C. Weiss, Shuigeng Zhou, and Toby Walsh. 2022. Fairness amidst non-iid graph data: A literature review. arXiv preprint arXiv:2202.07170 (2022).","journal-title":"arXiv preprint arXiv:2202.07170"},{"key":"e_1_3_1_32_2","article-title":"A survey on fairness for machine learning on graphs","author":"Choudhary Manvi","year":"2022","unstructured":"Manvi Choudhary, Charlotte Laclau, and Christine Largeron. 2022. A survey on fairness for machine learning on graphs. arXiv preprint arXiv:2205.05396 (2022).","journal-title":"arXiv preprint arXiv:2205.05396"},{"key":"e_1_3_1_33_2","article-title":"FMP: Toward fair graph message passing against topology bias","author":"Jiang Zhimeng","year":"2022","unstructured":"Zhimeng Jiang, Xiaotian Han, Chao Fan, Zirui Liu, Na Zou, Ali Mostafavi, and Xia Hu. 2022. FMP: Toward fair graph message passing against topology bias. arXiv preprint arXiv:2202.04187 (2022).","journal-title":"arXiv preprint arXiv:2202.04187"},{"key":"e_1_3_1_34_2","article-title":"Geometric deep learning: Grids, groups, graphs, geodesics, and gauges","author":"Bronstein Michael M.","year":"2021","unstructured":"Michael M. Bronstein, Joan Bruna, Taco Cohen, and Petar Veli\u010dkovi\u0107. 2021. Geometric deep learning: Grids, groups, graphs, geodesics, and gauges. arXiv:2104.13478 (2021).","journal-title":"arXiv:2104.13478"},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2878247"},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512173"},{"key":"e_1_3_1_37_2","article-title":"FairGAT: Fairness-aware graph attention networks","author":"Kose O. Deniz","year":"2023","unstructured":"O. Deniz Kose and Yanning Shen. 2023. FairGAT: Fairness-aware graph attention networks. arXiv preprint arXiv:2303.14591 (2023).","journal-title":"arXiv preprint arXiv:2303.14591"},{"key":"e_1_3_1_38_2","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539404"},{"key":"e_1_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441752"},{"key":"e_1_3_1_40_2","doi-asserted-by":"publisher","DOI":"10.5555\/2444851.2444861"},{"key":"e_1_3_1_41_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, 1774\u20131782."},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512189"},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3450015"},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-86520-7_22"},{"key":"e_1_3_1_45_2","article-title":"FairEdit: Preserving fairness in graph neural networks through greedy graph editing","author":"Loveland Donald","year":"2022","unstructured":"Donald Loveland, Jiayi Pan, Aaresh Farrokh Bhathena, and Yiyang Lu. 2022. FairEdit: Preserving fairness in graph neural networks through greedy graph editing. arXiv preprint arXiv:2201.03681 (2022).","journal-title":"arXiv preprint arXiv:2201.03681"},{"key":"e_1_3_1_46_2","article-title":"FairMod: Fair link prediction and recommendation via graph modification","author":"Current Sean","year":"2022","unstructured":"Sean Current, Yuntian He, Saket Gurukar, and Srinivasan Parthasarathy. 2022. FairMod: Fair link prediction and recommendation via graph modification. arXiv preprint arXiv:2201.11596 (2022).","journal-title":"arXiv preprint arXiv:2201.11596"},{"key":"e_1_3_1_47_2","first-page":"1","article-title":"HM-EIICT: Fairness-aware link prediction in complex networks using community information","author":"Saxena Akrati","year":"2021","unstructured":"Akrati Saxena, George Fletcher, and Mykola Pechenizkiy. 2021. HM-EIICT: Fairness-aware link prediction in complex networks using community information. Journal of Combinatorial Optimization (2021), 1\u201318.","journal-title":"Journal of Combinatorial Optimization"},{"key":"e_1_3_1_48_2","article-title":"On generalized degree fairness in graph neural networks","author":"Liu Zemin","year":"2023","unstructured":"Zemin Liu, Trung-Kien Nguyen, and Yuan Fang. 2023. On generalized degree fairness in graph neural networks. arXiv:2302.03881 (2023).","journal-title":"arXiv:2302.03881"},{"key":"e_1_3_1_49_2","article-title":"BeMap: Balanced message passing for fair graph neural network","author":"Lin Xiao","year":"2023","unstructured":"Xiao Lin, Jian Kang, Weilin Cong, and Hanghang Tong. 2023. BeMap: Balanced message passing for fair graph neural network. arXiv preprint arXiv:2306.04107 (2023).","journal-title":"arXiv preprint arXiv:2306.04107"},{"key":"e_1_3_1_50_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, 715\u2013724."},{"key":"e_1_3_1_51_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5429"},{"key":"e_1_3_1_52_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.595"},{"key":"e_1_3_1_53_2","doi-asserted-by":"publisher","DOI":"10.1109\/ASONAM49781.2020.9381348"},{"key":"e_1_3_1_54_2","article-title":"CrossWalk: Fairness-enhanced node representation learning","author":"Khajehnejad Ahmad","year":"2021","unstructured":"Ahmad Khajehnejad, Moein Khajehnejad, Mahmoudreza Babaei, Krishna P. Gummadi, Adrian Weller, and Baharan Mirzasoleiman. 2021. CrossWalk: Fairness-enhanced node representation learning. arXiv preprint arXiv:2105.02725 (2021).","journal-title":"arXiv preprint arXiv:2105.02725"},{"key":"e_1_3_1_55_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403080"},{"key":"e_1_3_1_56_2","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467266"},{"key":"e_1_3_1_57_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-30675-4_22"},{"key":"e_1_3_1_58_2","article-title":"Adversarial learning for debiasing knowledge graph embeddings","author":"Arduini Mario","year":"2020","unstructured":"Mario Arduini, Lorenzo Noci, Federico Pirovano, Ce Zhang, Yash Raj Shrestha, and Bibek Paudel. 2020. Adversarial learning for debiasing knowledge graph embeddings. arXiv preprint arXiv:2006.16309 (2020).","journal-title":"arXiv preprint arXiv:2006.16309"},{"key":"e_1_3_1_59_2","doi-asserted-by":"publisher","unstructured":"Tianxin Wei Yuning You Tianlong Chen Yang Shen Jingrui He and Zhangyang Wang. 2022. Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative. (2022). DOI:10.48550\/ARXIV.2210.03801","DOI":"10.48550\/ARXIV.2210.03801"},{"key":"e_1_3_1_60_2","volume-title":"NeurIPS 2022 Workshop: New Frontiers in Graph Learning","author":"Song Zixing","year":"2022","unstructured":"Zixing Song, Yueen Ma, and Irwin King. 2022. Individual fairness in dynamic financial networks. In NeurIPS 2022 Workshop: New Frontiers in Graph Learning."},{"key":"e_1_3_1_61_2","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599462"},{"key":"e_1_3_1_62_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i6.25905"},{"key":"e_1_3_1_63_2","volume-title":"The 11th International Conference on Learning Representations","author":"Ling Hongyi","year":"2022","unstructured":"Hongyi Ling, Zhimeng Jiang, Youzhi Luo, Shuiwang Ji, and Na Zou. 2022. Learning fair graph representations via automated data augmentations. In The 11th International Conference on Learning Representations."},{"key":"e_1_3_1_64_2","article-title":"Learning fair node representations with graph counterfactual fairness","author":"Ma Jing","year":"2022","unstructured":"Jing Ma, Ruocheng Guo, Mengting Wan, Longqi Yang, Aidong Zhang, and Jundong Li. 2022. Learning fair node representations with graph counterfactual fairness. arXiv preprint arXiv:2201.03662 (2022).","journal-title":"arXiv preprint arXiv:2201.03662"},{"key":"e_1_3_1_65_2","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. Advances in Neural Information Processing Systems 29 (2016).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_66_2","article-title":"Obtaining dyadic fairness by optimal transport","author":"Yang Moyi","year":"2022","unstructured":"Moyi Yang, Junjie Sheng, Xiangfeng Wang, Wenyan Liu, Bo Jin, Jun Wang, and Hongyuan Zha. 2022. Obtaining dyadic fairness by optimal transport. arXiv preprint arXiv:2202.04520 (2022).","journal-title":"arXiv preprint arXiv:2202.04520"},{"key":"e_1_3_1_67_2","doi-asserted-by":"publisher","DOI":"10.1145\/3363574"},{"key":"e_1_3_1_68_2","doi-asserted-by":"publisher","DOI":"10.1145\/3543873.3587369"},{"key":"e_1_3_1_69_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW58026.2022.00119"},{"key":"e_1_3_1_70_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.108058"},{"key":"e_1_3_1_71_2","article-title":"Adversarial graph embeddings for fair influence maximization over social networks","author":"Khajehnejad Moein","year":"2020","unstructured":"Moein Khajehnejad, Ahmad Asgharian Rezaei, Mahmoudreza Babaei, Jessica Hoffmann, Mahdi Jalili, and Adrian Weller. 2020. Adversarial graph embeddings for fair influence maximization over social networks. arXiv preprint arXiv:2005.04074 (2020).","journal-title":"arXiv preprint arXiv:2005.04074"},{"key":"e_1_3_1_72_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA52953.2021.00067"},{"key":"e_1_3_1_73_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.119064"},{"key":"e_1_3_1_74_2","doi-asserted-by":"publisher","DOI":"10.1109\/ASONAM55673.2022.10068703"},{"key":"e_1_3_1_75_2","article-title":"GNNUERS: Fairness explanation in GNNs for recommendation via counterfactual reasoning","author":"Medda Giacomo","year":"2023","unstructured":"Giacomo Medda, Francesco Fabbri, Mirko Marras, Ludovico Boratto, Mihnea Tufis, and Gianni Fenu. 2023. GNNUERS: Fairness explanation in GNNs for recommendation via counterfactual reasoning. arXiv preprint arXiv:2304.06182 (2023).","journal-title":"arXiv preprint arXiv:2304.06182"},{"key":"e_1_3_1_76_2","doi-asserted-by":"publisher","DOI":"10.3390\/info14020131"},{"key":"e_1_3_1_77_2","article-title":"FASTER: A dynamic fairness-assurance strategy for session-based recommender systems","author":"Wu Yao","year":"2023","unstructured":"Yao Wu, Jian Cao, and Guandong Xu. 2023. FASTER: A dynamic fairness-assurance strategy for session-based recommender systems. ACM Transactions on Information Systems (2023).","journal-title":"ACM Transactions on Information Systems"},{"key":"e_1_3_1_78_2","article-title":"Personalizing fairness-aware re-ranking","author":"Liu Weiwen","year":"2018","unstructured":"Weiwen Liu and Robin Burke. 2018. Personalizing fairness-aware re-ranking. arXiv preprint arXiv:1809.02921 (2018).","journal-title":"arXiv preprint arXiv:1809.02921"},{"key":"e_1_3_1_79_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2020.102579"},{"key":"e_1_3_1_80_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462966"},{"key":"e_1_3_1_81_2","doi-asserted-by":"publisher","DOI":"10.1145\/3495163"},{"key":"e_1_3_1_82_2","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3450065"},{"key":"e_1_3_1_83_2","doi-asserted-by":"publisher","DOI":"10.1609\/icwsm.v15i1.18111"},{"key":"e_1_3_1_84_2","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1007\/978-3-030-75015-2_15","volume-title":"Trends and Applications in Knowledge Discovery and Data Mining: PAKDD Workshops","author":"Vannur Lingraj S.","year":"2021","unstructured":"Lingraj S. Vannur, Balaji Ganesan, Lokesh Nagalapatti, Hima Patel, and M. N. Tippeswamy. 2021. Data augmentation for fairness in personal knowledge base population. In Trends and Applications in Knowledge Discovery and Data Mining: PAKDD Workshops. Springer, 143\u2013152."},{"key":"e_1_3_1_85_2","article-title":"HighAir: A hierarchical graph neural network-based air quality forecasting method","author":"Xu Jiahui","year":"2021","unstructured":"Jiahui Xu, Ling Chen, Mingqi Lv, Chaoqun Zhan, Sanjian Chen, and Jian Chang. 2021. HighAir: A hierarchical graph neural network-based air quality forecasting method. arXiv preprint arXiv:2101.04264 (2021).","journal-title":"arXiv preprint arXiv:2101.04264"},{"key":"e_1_3_1_86_2","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411872"},{"key":"e_1_3_1_87_2","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512169"},{"key":"e_1_3_1_88_2","article-title":"Fairness-aware graph filter design","author":"Kose O. Deniz","year":"2023","unstructured":"O. Deniz Kose, Yanning Shen, and Gonzalo Mateos. 2023. Fairness-aware graph filter design. arXiv preprint arXiv:2303.11459 (2023).","journal-title":"arXiv preprint arXiv:2303.11459"},{"key":"e_1_3_1_89_2","first-page":"610","volume-title":"International Conference on Complex Networks and Their Applications","author":"Krasanakis Emmanouil","year":"2020","unstructured":"Emmanouil Krasanakis, Symeon Papadopoulos, and Ioannis Kompatsiaris. 2020. Applying fairness constraints on graph node ranks under personalization bias. In International Conference on Complex Networks and Their Applications. Springer, 610\u2013622."},{"key":"e_1_3_1_90_2","volume-title":"AAAI","author":"Rossi Ryan A.","year":"2015","unstructured":"Ryan A. Rossi and Nesreen K. Ahmed. 2015. The network data repository with interactive graph analytics and visualization. In AAAI. https:\/\/networkrepository.com"},{"key":"e_1_3_1_91_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26344"},{"key":"e_1_3_1_92_2","doi-asserted-by":"crossref","unstructured":"Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable Feature Learning for Networks. https:\/\/arxiv.org\/abs\/1607.00653","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_3_1_93_2","article-title":"Ensuring user-side fairness in dynamic recommender systems","author":"Yoo Hyunsik","year":"2023","unstructured":"Hyunsik Yoo, Zhichen Zeng, Jian Kang, Zhining Liu, David Zhou, Fei Wang, Eunice Chan, and Hanghang Tong. 2023. Ensuring user-side fairness in dynamic recommender systems. arXiv preprint arXiv:2308.15651 (2023).","journal-title":"arXiv preprint arXiv:2308.15651"},{"key":"e_1_3_1_94_2","article-title":"Demystifying structural disparity in graph neural networks: can one size fit all?","author":"Mao Haitao","year":"2023","unstructured":"Haitao Mao, Zhikai Chen, Wei Jin, Haoyu Han, Yao Ma, Tong Zhao, Neil Shah, and Jiliang Tang. 2023. Demystifying structural disparity in graph neural networks: can one size fit all? arXiv preprint arXiv:2306.01323 (2023).","journal-title":"arXiv preprint arXiv:2306.01323"},{"key":"e_1_3_1_95_2","article-title":"A topological perspective on demystifying GNN-based link prediction performance","author":"Wang Yu","year":"2023","unstructured":"Yu Wang, Tong Zhao, Yuying Zhao, Yunchao Liu, Xueqi Cheng, Neil Shah, and Tyler Derr. 2023. A topological perspective on demystifying GNN-based link prediction performance. arXiv preprint arXiv:2310.04612 (2023).","journal-title":"arXiv preprint arXiv:2310.04612"},{"key":"e_1_3_1_96_2","article-title":"Revisiting link prediction: A data perspective","author":"Mao Haitao","year":"2023","unstructured":"Haitao Mao, Juanhui Li, Harry Shomer, Bingheng Li, Wenqi Fan, Yao Ma, Tong Zhao, Neil Shah, and Jiliang Tang. 2023. Revisiting link prediction: A data perspective. arXiv preprint arXiv:2310.00793 (2023).","journal-title":"arXiv preprint arXiv:2310.00793"},{"key":"e_1_3_1_97_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW58026.2022.00103"},{"key":"e_1_3_1_98_2","unstructured":"Yuying Zhao Yu Wang Yi Zhang Pamela Wisniewski Charu Aggarwal and Tyler Derr. 2018. Fair online dating recommendations for sexually fluid users via leveraging opposite gender interaction ratio. https:\/\/arxiv.org\/abs\/2402.12541"},{"key":"e_1_3_1_99_2","doi-asserted-by":"publisher","DOI":"10.1109\/BigData55660.2022.10020548"},{"key":"e_1_3_1_100_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2016.7498311"},{"key":"e_1_3_1_101_2","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599321"},{"key":"e_1_3_1_102_2","article-title":"Graph neural prompting with large language models","author":"Tian Yijun","year":"2023","unstructured":"Yijun Tian, Huan Song, Zichen Wang, Haozhu Wang, Ziqing Hu, Fang Wang, Nitesh V. Chawla, and Panpan Xu. 2023. Graph neural prompting with large language models. arXiv preprint arXiv:2309.15427 (2023).","journal-title":"arXiv preprint arXiv:2309.15427"},{"key":"e_1_3_1_103_2","article-title":"Grape: Knowledge graph enhanced passage reader for open-domain question answering","author":"Ju Mingxuan","year":"2022","unstructured":"Mingxuan Ju, Wenhao Yu, Tong Zhao, Chuxu Zhang, and Yanfang Ye. 2022. Grape: Knowledge graph enhanced passage reader for open-domain question answering. arXiv preprint arXiv:2210.02933 (2022).","journal-title":"arXiv preprint arXiv:2210.02933"},{"key":"e_1_3_1_104_2","article-title":"KG-FID: Infusing knowledge graph in fusion-in-decoder for open-domain question answering","author":"Yu Donghan","year":"2021","unstructured":"Donghan Yu, Chenguang Zhu, Yuwei Fang, Wenhao Yu, Shuohang Wang, Yichong Xu, Xiang Ren, Yiming Yang, and Michael Zeng. 2021. KG-FID: Infusing knowledge graph in fusion-in-decoder for open-domain question answering. arXiv preprint arXiv:2110.04330 (2021).","journal-title":"arXiv preprint arXiv:2110.04330"},{"key":"e_1_3_1_105_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.126792"},{"key":"e_1_3_1_106_2","first-page":"37309","article-title":"Deep bidirectional language-knowledge graph pretraining","volume":"35","author":"Yasunaga Michihiro","year":"2022","unstructured":"Michihiro Yasunaga, Antoine Bosselut, Hongyu Ren, Xikun Zhang, Christopher D. Manning, Percy S. Liang, and Jure Leskovec. 2022. Deep bidirectional language-knowledge graph pretraining. Advances in Neural Information Processing Systems 35 (2022), 37309\u201337323.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_107_2","doi-asserted-by":"publisher","DOI":"10.1145\/3289600.3290956"},{"key":"e_1_3_1_108_2","article-title":"Investigating pretrained language models for graph-to-text generation","author":"Ribeiro Leonardo F. R.","year":"2020","unstructured":"Leonardo F. R. Ribeiro, Martin Schmitt, Hinrich Sch\u00fctze, and Iryna Gurevych. 2020. Investigating pretrained language models for graph-to-text generation. arXiv preprint arXiv:2007.08426 (2020).","journal-title":"arXiv preprint arXiv:2007.08426"},{"key":"e_1_3_1_109_2","article-title":"Text generation from knowledge graphs with graph transformers","author":"Koncel-Kedziorski Rik","year":"2019","unstructured":"Rik Koncel-Kedziorski, Dhanush Bekal, Yi Luan, Mirella Lapata, and Hannaneh Hajishirzi. 2019. Text generation from knowledge graphs with graph transformers. arXiv preprint arXiv:1904.02342 (2019).","journal-title":"arXiv preprint arXiv:1904.02342"},{"key":"e_1_3_1_110_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3168775"},{"key":"e_1_3_1_111_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3030076"},{"key":"e_1_3_1_112_2","article-title":"Bias and fairness in large language models: A survey","author":"Gallegos Isabel O.","year":"2023","unstructured":"Isabel O. Gallegos, Ryan A. Rossi, Joe Barrow, Md Mehrab Tanjim, Sungchul Kim, Franck Dernoncourt, Tong Yu, Ruiyi Zhang, and Nesreen K. Ahmed. 2023. Bias and fairness in large language models: A survey. arXiv preprint arXiv:2309.00770 (2023).","journal-title":"arXiv preprint arXiv:2309.00770"},{"key":"e_1_3_1_113_2","article-title":"Is ChatGPT fair for recommendation? Evaluating fairness in large language model recommendation","author":"Zhang Jizhi","year":"2023","unstructured":"Jizhi Zhang, Keqin Bao, Yang Zhang, Wenjie Wang, Fuli Feng, and Xiangnan He. 2023. Is ChatGPT fair for recommendation? Evaluating fairness in large language model recommendation. arXiv preprint arXiv:2305.07609 (2023).","journal-title":"arXiv preprint arXiv:2305.07609"},{"key":"e_1_3_1_114_2","article-title":"Knowledge graph prompting for multi-document question answering","author":"Wang Yu","year":"2023","unstructured":"Yu Wang, Nedim Lipka, Ryan A. Rossi, Alexa Siu, Ruiyi Zhang, and Tyler Derr. 2023. Knowledge graph prompting for multi-document question answering. arXiv preprint arXiv:2308.11730 (2023).","journal-title":"arXiv preprint arXiv:2308.11730"},{"key":"e_1_3_1_115_2","article-title":"Tree of thoughts: Deliberate problem solving with large language models","author":"Yao Shunyu","year":"2023","unstructured":"Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, and Karthik Narasimhan. 2023. Tree of thoughts: Deliberate problem solving with large language models. arXiv preprint arXiv:2305.10601 (2023).","journal-title":"arXiv preprint arXiv:2305.10601"},{"key":"e_1_3_1_116_2","first-page":"24824","article-title":"Chain-of-thought prompting elicits reasoning in large language models","volume":"35","author":"Wei Jason","year":"2022","unstructured":"Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V. Le, Denny Zhou, et\u00a0al. 2022. Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems 35 (2022), 24824\u201324837.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_117_2","article-title":"Graph of thoughts: Solving elaborate problems with large language models","author":"Besta Maciej","year":"2023","unstructured":"Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Lukas Gianinazzi, Joanna Gajda, Tomasz Lehmann, Michal Podstawski, Hubert Niewiadomski, Piotr Nyczyk, et\u00a0al. 2023. Graph of thoughts: Solving elaborate problems with large language models. arXiv preprint arXiv:2308.09687 (2023).","journal-title":"arXiv preprint arXiv:2308.09687"},{"key":"e_1_3_1_118_2","article-title":"ToolChain*: Efficient action space navigation in large language models with A* search","author":"Zhuang Yuchen","year":"2023","unstructured":"Yuchen Zhuang, Xiang Chen, Tong Yu, Saayan Mitra, Victor Bursztyn, Ryan A. Rossi, Somdeb Sarkhel, and Chao Zhang. 2023. ToolChain*: Efficient action space navigation in large language models with A* search. arXiv preprint arXiv:2310.13227 (2023).","journal-title":"arXiv preprint arXiv:2310.13227"}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3649142","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3649142","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T22:50:01Z","timestamp":1750287001000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3649142"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,12]]},"references-count":117,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,7,31]]}},"alternative-id":["10.1145\/3649142"],"URL":"https:\/\/doi.org\/10.1145\/3649142","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"value":"1556-4681","type":"print"},{"value":"1556-472X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,12]]},"assertion":[{"value":"2023-07-08","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-02-11","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-04-12","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}