{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T14:29:50Z","timestamp":1782916190689,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":36,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,8,14]],"date-time":"2022-08-14T00:00:00Z","timestamp":1660435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"the Cisco Faculty Research Award"},{"name":"the National Science Foundation (NSF)","award":["grant No. 2006844"],"award-info":[{"award-number":["grant No. 2006844"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,8,14]]},"DOI":"10.1145\/3534678.3539404","type":"proceedings-article","created":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T19:06:12Z","timestamp":1660331172000},"page":"1938-1948","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":75,"title":["Improving Fairness in Graph Neural Networks via Mitigating Sensitive Attribute Leakage"],"prefix":"10.1145","author":[{"given":"Yu","family":"Wang","sequence":"first","affiliation":[{"name":"Vanderbilt University, Nashville, TN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuying","family":"Zhao","sequence":"additional","affiliation":[{"name":"Vanderbilt University, Nashville, TN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yushun","family":"Dong","sequence":"additional","affiliation":[{"name":"University of Virginia, Charlottesville, VA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huiyuan","family":"Chen","sequence":"additional","affiliation":[{"name":"Case Western Reserve University, Cleveland, OH, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jundong","family":"Li","sequence":"additional","affiliation":[{"name":"University of Virginia, Charlottesville, VA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tyler","family":"Derr","sequence":"additional","affiliation":[{"name":"Vanderbilt University, Nashville, TN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,8,14]]},"reference":[{"key":"e_1_3_2_1_1_1","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--2124."},{"key":"e_1_3_2_1_2_1","volume-title":"International Conference on Machine Learning. 715--724","author":"Bose Avishek","year":"2019","unstructured":"Avishek Bose and William Hamilton. 2019. Compositional fairness constraints for graph embeddings. In International Conference on Machine Learning. 715--724."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462868"},{"key":"e_1_3_2_1_4_1","volume-title":"Proceedings of the 37th International Conference on Machine Learning, ICML 2020.","author":"Chen Ming","year":"2020","unstructured":"Ming Chen, Zhewei Wei, Zengfeng Huang, Bolin Ding, and Yaliang Li. 2020. Simple and Deep Graph Convolutional Networks. In Proceedings of the 37th International Conference on Machine Learning, ICML 2020."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441752"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467266"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512173"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539319"},{"key":"e_1_3_2_1_9_1","volume-title":"Fairness in deep learning: A computational perspective","author":"Du Mengnan","year":"2020","unstructured":"Mengnan Du, Fan Yang, Na Zou, and Xia Hu. 2020. Fairness in deep learning: A computational perspective. IEEE Intelligent Systems (2020)."},{"key":"e_1_3_2_1_10_1","unstructured":"Adam Paszke et al. 2019. PyTorch: An Imperative Style High-Performance Deep Learning Library. In NeurIPS. 8024--8035."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"crossref","unstructured":"Wenqi Fan Yao Ma Qing Li Yuan He Eric Zhao Jiliang Tang and Dawei Yin. 2019. Graph neural networks for social recommendation. In WWW. 417--426.","DOI":"10.1145\/3308558.3313488"},{"key":"e_1_3_2_1_12_1","volume-title":"Generative adversarial nets. Advances in neural information processing systems","author":"Goodfellow Ian","year":"2014","unstructured":"Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. Advances in neural information processing systems , Vol. 27 (2014)."},{"key":"e_1_3_2_1_13_1","unstructured":"William L. Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In NeurIPS. 1024--1034."},{"key":"e_1_3_2_1_14_1","volume-title":"International Conference on Learning Representations.","author":"Jang Eric","year":"2017","unstructured":"Eric Jang, Shixiang Gu, and Ben Poole. 2017. Categorical reparameterization with gumbel-softmax. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_15_1","volume-title":"Self-supervised learning on graphs: Deep insights and new direction. arXiv preprint arXiv:2006.10141","author":"Jin Wei","year":"2020","unstructured":"Wei Jin, Tyler Derr, Haochen Liu, Yiqi Wang, Suhang Wang, Zitao Liu, and Jiliang Tang. 2020. Self-supervised learning on graphs: Deep insights and new direction. arXiv preprint arXiv:2006.10141 (2020)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539445"},{"key":"e_1_3_2_1_17_1","volume-title":"Graph Condensation for Graph Neural Networks. In 7th International Conference on Learning Representations, ICLR.","author":"Jin Wei","year":"2021","unstructured":"Wei Jin, Lingxiao Zhao, Shichang Zhang, Yozen Liu, Jiliang Tang, and Neil Shah. 2021. Graph Condensation for Graph Neural Networks. In 7th International Conference on Learning Representations, ICLR."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW.2011.83"},{"key":"e_1_3_2_1_19_1","volume-title":"Semi-Supervised Classification with Graph Convolutional Networks. In 5th International Conference on Learning Representations, ICLR.","author":"Thomas","unstructured":"Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In 5th International Conference on Learning Representations, ICLR."},{"key":"e_1_3_2_1_20_1","volume-title":"7th International Conference on Learning Representations, ICLR.","author":"Klicpera Johannes","year":"2019","unstructured":"Johannes Klicpera, Aleksandar Bojchevski, and Stephan G\u00fc nnemann. 2019. Predict then Propagate: Graph Neural Networks meet Personalized PageRank. In 7th International Conference on Learning Representations, ICLR."},{"key":"e_1_3_2_1_21_1","volume-title":"Fairness-Aware Node Representation Learning. arXiv preprint arXiv:2106.05391","author":"K\u00f6se \u00d6yk\u00fc Deniz","year":"2021","unstructured":"\u00d6yk\u00fc Deniz K\u00f6se and Yanning Shen. 2021. Fairness-Aware Node Representation Learning. arXiv preprint arXiv:2106.05391 (2021)."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403076"},{"key":"e_1_3_2_1_23_1","volume-title":"Birds of a feather: Homophily in social networks. Annual review of sociology","author":"McPherson Miller","year":"2001","unstructured":"Miller McPherson, Lynn Smith-Lovin, and James M Cook. 2001. Birds of a feather: Homophily in social networks. Annual review of sociology , Vol. 27, 1 (2001), 415--444."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3457607"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539023"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0197-0"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1007\/s41060-021-00247-3"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411872"},{"key":"e_1_3_2_1_29_1","volume-title":"Distance-wise Prototypical Graph Neural Network in Node Imbalance Classification. arXiv preprint arXiv:2110.12035","author":"Wang Yu","year":"2021","unstructured":"Yu Wang, Charu Aggarwal, and Tyler Derr. 2021. Distance-wise Prototypical Graph Neural Network in Node Imbalance Classification. arXiv preprint arXiv:2110.12035 (2021)."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482487"},{"key":"e_1_3_2_1_31_1","volume-title":"Graph Neural Networks: Foundations, Frontiers, and Applications","author":"Wang Yu","unstructured":"Yu Wang, Wei Jin, and Tyler Derr. 2022. Graph Neural Networks: Self-supervised Learning. In Graph Neural Networks: Foundations, Frontiers, and Applications. Springer, 391--420."},{"key":"e_1_3_2_1_32_1","unstructured":"Asiri Wijesinghe and Qing Wang. 2022. A New Perspective on \u201dHow Graph Neural Networks Go Beyond Weisfeiler-Lehman?\u201d. In ICLR."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16582"},{"key":"e_1_3_2_1_34_1","volume-title":"7th International Conference on Learning Representations, ICLR 2019","author":"Xu Keyulu","year":"2019","unstructured":"Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019. How Powerful are Graph Neural Networks?. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6--9, 2019."},{"key":"e_1_3_2_1_35_1","volume-title":"Advances in Neural Information Processing Systems","volume":"31","author":"Zhang Muhan","year":"2018","unstructured":"Muhan Zhang and Yixin Chen. 2018. Link prediction based on graph neural networks. Advances in Neural Information Processing Systems , Vol. 31 (2018)."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3488560.3498493"}],"event":{"name":"KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Washington DC USA","acronym":"KDD '22","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 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3539404","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3534678.3539404","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:02:48Z","timestamp":1750186968000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3539404"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,14]]},"references-count":36,"alternative-id":["10.1145\/3534678.3539404","10.1145\/3534678"],"URL":"https:\/\/doi.org\/10.1145\/3534678.3539404","relation":{},"subject":[],"published":{"date-parts":[[2022,8,14]]},"assertion":[{"value":"2022-08-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}