{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T17:21:06Z","timestamp":1777051266607,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":32,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,4,25]],"date-time":"2022-04-25T00:00:00Z","timestamp":1650844800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["1947135,1939725"],"award-info":[{"award-number":["1947135,1939725"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000183","name":"Army Research Office","doi-asserted-by":"publisher","award":["W911NF2110088"],"award-info":[{"award-number":["W911NF2110088"]}],"id":[{"id":"10.13039\/100000183","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000185","name":"Defense Advanced Research Projects Agency","doi-asserted-by":"publisher","award":["HR001121C0165"],"award-info":[{"award-number":["HR001121C0165"]}],"id":[{"id":"10.13039\/100000185","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,4,25]]},"DOI":"10.1145\/3485447.3512169","type":"proceedings-article","created":{"date-parts":[[2022,4,25]],"date-time":"2022-04-25T05:13:07Z","timestamp":1650863587000},"page":"1214-1225","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":32,"title":["RawlsGCN: Towards Rawlsian Difference Principle on Graph Convolutional Network"],"prefix":"10.1145","author":[{"given":"Jian","family":"Kang","sequence":"first","affiliation":[{"name":"University of Illinois at Urbana-Champaign, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Zhu","sequence":"additional","affiliation":[{"name":"Facebook AI, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yinglong","family":"Xia","sequence":"additional","affiliation":[{"name":"Facebook AI, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiebo","family":"Luo","sequence":"additional","affiliation":[{"name":"University of Rochester, USA and Facebook AI, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanghang","family":"Tong","sequence":"additional","affiliation":[{"name":"University of Illinois at Urbana-Champaign, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,4,25]]},"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. arXiv preprint arXiv:2102.13186(2021)."},{"key":"e_1_3_2_1_2_1","unstructured":"James Atwood and Don Towsley. 2016. Diffusion-Convolutional Neural Networks. In Advances in Neural Information Processing Systems. 1993\u20132001."},{"key":"e_1_3_2_1_3_1","volume-title":"Compositional Fairness Constraints for Graph Embeddings. In International Conference on Machine Learning. 715\u2013724","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\u2013724."},{"key":"e_1_3_2_1_4_1","unstructured":"Joan Bruna Wojciech Zaremba Arthur Szlam and Yann LeCun. 2013. Spectral Networks and Locally Connected Networks on Graphs. arXiv preprint arXiv:1312.6203(2013)."},{"key":"e_1_3_2_1_5_1","volume-title":"DeBayes: A Bayesian Method for Debiasing Network Embeddings. In International Conference on Machine Learning. 1220\u20131229","author":"Buyl Maarten","year":"2020","unstructured":"Maarten Buyl and Tijl De\u00a0Bie. 2020. DeBayes: A Bayesian Method for Debiasing Network Embeddings. In International Conference on Machine Learning. 1220\u20131229."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.chemmater.9b01294"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441752"},{"key":"e_1_3_2_1_8_1","volume-title":"Proceedings of the 29th ACM International Conference on Information & Knowledge Management.","author":"Derrow-Pinion Austin","year":"2021","unstructured":"Austin Derrow-Pinion, Jennifer She, David Wong, Oliver Lange, Todd Hester, Luis Perez, Marc Nunkesser, Seongjae Lee, Xueying Guo, Brett Wiltshire, 2021. ETA Prediction with Graph Neural Networks in Google Maps. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467266"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_3_2_1_11_1","unstructured":"Kai Guo Kaixiong Zhou Xia Hu Yu Li Yi Chang and Xin Wang. 2021. Orthogonal Graph Neural Networks. arXiv preprint arXiv:2109.11338(2021)."},{"key":"e_1_3_2_1_12_1","unstructured":"William\u00a0L Hamilton Rex Ying and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In Advances in Neural Information Processing Systems. 1025\u20131035."},{"key":"e_1_3_2_1_13_1","volume-title":"International Conference on Machine Learning. 1929\u20131938","author":"Hashimoto Tatsunori","year":"2018","unstructured":"Tatsunori Hashimoto, Megha Srivastava, Hongseok Namkoong, and Percy Liang. 2018. Fairness without Demographics in Repeated Loss Minimization. In International Conference on Machine Learning. 1929\u20131938."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449971"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449969"},{"key":"e_1_3_2_1_16_1","volume-title":"Conditional Network Embeddings. In International Conference on Learning Representations.","author":"Kang Bo","year":"2018","unstructured":"Bo Kang, Jefrey Lijffijt, and Tijl De\u00a0Bie. 2018. Conditional Network Embeddings. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403080"},{"key":"e_1_3_2_1_18_1","volume-title":"Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations.","author":"N.","unstructured":"Thomas\u00a0N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_19_1","volume-title":"Guarantees for Spectral Clustering with Fairness Constraints. In International Conference on Machine Learning. 3458\u20133467","author":"Kleindessner Matth\u00e4us","year":"2019","unstructured":"Matth\u00e4us Kleindessner, Samira Samadi, Pranjal Awasthi, and Jamie Morgenstern. 2019. Guarantees for Spectral Clustering with Fairness Constraints. In International Conference on Machine Learning. 3458\u20133467."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467276"},{"key":"e_1_3_2_1_21_1","volume-title":"Geom-GCN: Geometric Graph Convolutional Networks. In International Conference on Learning Representations.","author":"Pei Hongbin","year":"2019","unstructured":"Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, and Bo Yang. 2019. Geom-GCN: Geometric Graph Convolutional Networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.2016.1500104NM"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/456"},{"key":"e_1_3_2_1_24_1","first-page":"15776","article-title":"Exploring Algorithmic Fairness in Robust Graph Covering Problems","volume":"32","author":"Rahmattalabi Aida","year":"2019","unstructured":"Aida Rahmattalabi, Phebe Vayanos, Anthony Fulginiti, Eric Rice, Bryan Wilder, Amulya Yadav, and Milind Tambe. 2019. Exploring Algorithmic Fairness in Robust Graph Covering Problems. Advances in Neural Information Processing Systems 32 (2019), 15776\u201315787.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_25_1","volume-title":"A Theory of Justice","author":"Rawls John","unstructured":"John Rawls. 1971. A Theory of Justice. Harvard University Press."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.2140\/pjm.1967.21.343"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411872"},{"key":"e_1_3_2_1_28_1","volume-title":"Fairness-Aware PageRank. In Proceedings of the Web Conference","author":"Tsioutsiouliklis Sotiris","year":"2021","unstructured":"Sotiris Tsioutsiouliklis, Evaggelia Pitoura, Panayiotis Tsaparas, Ilias Kleftakis, and Nikos Mamoulis. 2021. Fairness-Aware PageRank. In Proceedings of the Web Conference 2021. 3815\u20133826."},{"key":"e_1_3_2_1_29_1","volume-title":"Graph Attention Networks. In 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 International Conference on Learning Representations."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331267"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330950"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611974973.64"}],"event":{"name":"WWW '22: The ACM Web Conference 2022","location":"Virtual Event, Lyon France","acronym":"WWW '22","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of the ACM Web Conference 2022"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3485447.3512169","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3485447.3512169","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3485447.3512169","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:31:14Z","timestamp":1750188674000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3485447.3512169"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,25]]},"references-count":32,"alternative-id":["10.1145\/3485447.3512169","10.1145\/3485447"],"URL":"https:\/\/doi.org\/10.1145\/3485447.3512169","relation":{},"subject":[],"published":{"date-parts":[[2022,4,25]]},"assertion":[{"value":"2022-04-25","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}