{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T23:14:28Z","timestamp":1771024468544,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":33,"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":"Cisco Faculty Research Award"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,8,14]]},"DOI":"10.1145\/3534678.3539346","type":"proceedings-article","created":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T19:06:41Z","timestamp":1660331201000},"page":"1625-1634","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":23,"title":["GUIDE: Group Equality Informed Individual Fairness in Graph Neural Networks"],"prefix":"10.1145","author":[{"given":"Weihao","family":"Song","sequence":"first","affiliation":[{"name":"University of Virginia, Charlottesville, VA, USA"}]},{"given":"Yushun","family":"Dong","sequence":"additional","affiliation":[{"name":"University of Virginia, Charlottesville, VA, USA"}]},{"given":"Ninghao","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Georgia, Athens, GA, USA"}]},{"given":"Jundong","family":"Li","sequence":"additional","affiliation":[{"name":"University of Virginia, Charlottesville, VA, USA"}]}],"member":"320","published-online":{"date-parts":[[2022,8,14]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Towards a Unified Framework for Fair and Stable Graph Representation Learning. CoRR","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. CoRR, Vol. abs\/2102.13186 (2021). arxiv: 2102.13186 https:\/\/arxiv.org\/abs\/2102.13186"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1178"},{"key":"e_1_3_2_1_3_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9--15","volume":"724","author":"Bose Avishek Joey","year":"2019","unstructured":"Avishek Joey Bose and William L. Hamilton. 2019. Compositional Fairness Constraints for Graph Embeddings. In Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9--15 June 2019, Long Beach, California, USA (Proceedings of Machine Learning Research, Vol. 97). PMLR, 715--724."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441752"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467266"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512173"},{"key":"e_1_3_2_1_7_1","volume-title":"Fairness in Graph Mining: A Survey. arXiv preprint arXiv:2204.09888","author":"Dong Yushun","year":"2022","unstructured":"Yushun Dong, Jing Ma, Chen Chen, and Jundong Li. 2022b. Fairness in Graph Mining: A Survey. arXiv preprint arXiv:2204.09888 (2022)."},{"key":"e_1_3_2_1_8_1","unstructured":"Dheeru Dua and Casey Graff. 2017. UCI Machine Learning Repository. http:\/\/archive.ics.uci.edu\/ml"},{"key":"e_1_3_2_1_9_1","volume-title":"Zemel","author":"Dwork Cynthia","year":"2012","unstructured":"Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard S. Zemel. 2012. Fairness through awareness. In Innovations in Theoretical Computer Science 2012, Cambridge, MA, USA, January 8--10, 2012. ACM, 214--226."},{"key":"e_1_3_2_1_10_1","volume-title":"Maxmin-Fair Ranking: Individual Fairness under Group-Fairness Constraints. CoRR","author":"Soriano David Garc'i","year":"2021","unstructured":"David Garc'i a-Soriano and Francesco Bonchi. 2021. Maxmin-Fair Ranking: Individual Fairness under Group-Fairness Constraints. CoRR, Vol. abs\/2106.08652 (2021). https:\/\/arxiv.org\/abs\/2106.08652"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371770"},{"key":"e_1_3_2_1_12_1","volume-title":"Inductive Representation Learning on Large Graphs. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017","author":"Hamilton William L.","year":"2017","unstructured":"William L. Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4--9, 2017, Long Beach, CA, USA. 1024--1034."},{"key":"e_1_3_2_1_13_1","volume-title":"Equality of Opportunity in Supervised Learning. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016","author":"Hardt Moritz","year":"2016","unstructured":"Moritz Hardt, Eric Price, and Nati Srebro. 2016. Equality of Opportunity in Supervised Learning. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5--10, 2016, Barcelona, Spain. 3315--3323."},{"key":"e_1_3_2_1_14_1","volume-title":"Zaki","author":"Hasan Mohammad Al","year":"2011","unstructured":"Mohammad Al Hasan and Mohammed J. Zaki. 2011. A Survey of Link Prediction in Social Networks. In Social Network Data Analytics, , Charu C. Aggarwal (Ed.). Springer, 243--275."},{"key":"e_1_3_2_1_15_1","volume-title":"Yu","author":"Ji Shaoxiong","year":"2020","unstructured":"Shaoxiong Ji, Shirui Pan, Erik Cambria, Pekka Marttinen, and Philip S. Yu. 2020. A Survey on Knowledge Graphs: Representation, Acquisition and Applications. CoRR, Vol. abs\/2002.00388 (2020). https:\/\/arxiv.org\/abs\/2002.00388"},{"key":"e_1_3_2_1_16_1","volume-title":"InFoRM: Individual Fairness on Graph Mining. In KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"Kang Jian","year":"2020","unstructured":"Jian Kang, Jingrui He, Ross Maciejewski, and Hanghang Tong. 2020. InFoRM: Individual Fairness on Graph Mining. In KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, CA, USA, August 23--27, 2020. ACM, 379--389."},{"key":"e_1_3_2_1_17_1","volume-title":"Kingma and Jimmy Ba","author":"Diederik","year":"2015","unstructured":"Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7--9, 2015, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.). http:\/\/arxiv.org\/abs\/1412.6980"},{"key":"e_1_3_2_1_18_1","volume-title":"Kipf and Max Welling","author":"Thomas","year":"2017","unstructured":"Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24--26, 2017, Conference Track Proceedings. OpenReview.net."},{"key":"e_1_3_2_1_19_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9--15","volume":"3467","author":"Samadi Samira","year":"2019","unstructured":"Matth\"a us Kleindessner, Samira Samadi, Pranjal Awasthi, and Jamie Morgenstern. 2019. Guarantees for Spectral Clustering with Fairness Constraints. In Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9--15 June 2019, Long Beach, California, USA (Proceedings of Machine Learning Research, Vol. 97). PMLR, 3458--3467."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.14778\/3372716.3372723"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3457607"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/456"},{"key":"e_1_3_2_1_23_1","volume-title":"International Scientific Conference and International Workshop Present Day Trends of Innovations (01","author":"Takac L.","year":"2012","unstructured":"L. Takac and Michal Z\u00e1bovsk\u00fd. 2012. Data analysis in public social networks. International Scientific Conference and International Workshop Present Day Trends of Innovations (01 2012), 1--6."},{"key":"e_1_3_2_1_24_1","volume-title":"Graph Attention Networks. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net.","author":"Velickovic Petar","year":"2018","unstructured":"Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li\u00f2, and Yoshua Bengio. 2018a. Graph Attention Networks. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net."},{"key":"e_1_3_2_1_25_1","volume-title":"6th International Conference on Learning Representations, ICLR","author":"Velickovic Petar","year":"2018","unstructured":"Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li\u00f2, and Yoshua Bengio. 2018b. Graph Attention Networks. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net. https:\/\/openreview.net\/forum?id=rJXMpikCZ"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2019.00070"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3450025"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313442"},{"key":"e_1_3_2_1_29_1","volume-title":"Graph Neural Networks in Recommender Systems: A Survey. CoRR","author":"Wu Shiwen","year":"2020","unstructured":"Shiwen Wu, Wentao Zhang, Fei Sun, and Bin Cui. 2020. Graph Neural Networks in Recommender Systems: A Survey. CoRR, Vol. abs\/2011.02260 (2020). https:\/\/arxiv.org\/abs\/2011.02260"},{"key":"e_1_3_2_1_30_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. OpenReview.net."},{"key":"e_1_3_2_1_31_1","volume-title":"Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsm\"a ssan","author":"Xu Keyulu","year":"2018","unstructured":"Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, and Stefanie Jegelka. 2018. Representation Learning on Graphs with Jumping Knowledge Networks. In Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsm\"a ssan, Stockholm, Sweden, July 10--15, 2018 (Proceedings of Machine Learning Research, Vol. 80). PMLR, 5449--5458."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2007.12.020"},{"key":"e_1_3_2_1_33_1","volume-title":"Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017, 20--22","volume":"970","author":"Zafar Muhammad Bilal","year":"2017","unstructured":"Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez-Rodriguez, and Krishna P. Gummadi. 2017. Fairness Constraints: Mechanisms for Fair Classification. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017, 20--22 April 2017, Fort Lauderdale, FL, USA (Proceedings of Machine Learning Research, Vol. 54). PMLR, 962--970."}],"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\/3534678.3539346","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3534678.3539346","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:02:47Z","timestamp":1750186967000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3539346"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,14]]},"references-count":33,"alternative-id":["10.1145\/3534678.3539346","10.1145\/3534678"],"URL":"https:\/\/doi.org\/10.1145\/3534678.3539346","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"}}]}}