{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T11:13:41Z","timestamp":1730200421584,"version":"3.28.0"},"reference-count":29,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,12,17]],"date-time":"2022-12-17T00:00:00Z","timestamp":1671235200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,12,17]],"date-time":"2022-12-17T00:00:00Z","timestamp":1671235200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,12,17]]},"DOI":"10.1109\/bigdata55660.2022.10020550","type":"proceedings-article","created":{"date-parts":[[2023,1,26]],"date-time":"2023-01-26T14:35:23Z","timestamp":1674743723000},"page":"4726-4732","source":"Crossref","is-referenced-by-count":1,"title":["Obtaining Dyadic Fairness by Optimal Transport"],"prefix":"10.1109","author":[{"given":"Moyi","family":"Yang","sequence":"first","affiliation":[{"name":"East China Normal University,School of Computer Science and Technology,Shanghai,China,200062"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junjie","family":"Sheng","sequence":"additional","affiliation":[{"name":"East China Normal University,School of Computer Science and Technology,Shanghai,China,200062"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenyan","family":"Liu","sequence":"additional","affiliation":[{"name":"East China Normal University,School of Computer Science and Technology,Shanghai,China,200062"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Jin","sequence":"additional","affiliation":[{"name":"East China Normal University,School of Computer Science and Technology,Shanghai,China,200062"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoling","family":"Wang","sequence":"additional","affiliation":[{"name":"East China Normal University,School of Computer Science and Technology,Shanghai,China,200062"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangfeng","family":"Wang","sequence":"additional","affiliation":[{"name":"East China Normal University,School of Computer Science and Technology,Shanghai,China,200062"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-71050-9"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-43883-8"},{"article-title":"Semi-Supervised Classification with Graph Convolutional Networks","year":"2017","author":"kipf","key":"ref15"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/3158369"},{"journal-title":"Fairness with continuous optimal transport","year":"2021","author":"chiappa","key":"ref11"},{"key":"ref10","first-page":"7321","article-title":"Fair regression with wasserstein barycenters[J]","volume":"33","author":"chzhen","year":"2020","journal-title":"Advances in neural information processing systems"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2021.3133818"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441752"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/456"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186140"},{"key":"ref18","first-page":"1220","article-title":"Debayes: a bayesian method for debiasing network embeddings[C]","author":"buyl","year":"2020","journal-title":"International Conference on Machine Learning"},{"key":"ref24","first-page":"254","author":"angwin","year":"2016","journal-title":"Ethics of Data and Analytics"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/2808797.2809407"},{"key":"ref26","article-title":"Amazon&#x2019;s Sexist AI Recruiting Tool: How Did It Go so Wrong?&#x2019;[J]","author":"lauret","year":"2019","journal-title":"Medium"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/2566486.2568012"},{"journal-title":"Confidence Intervals for Testing Disparate Impact in Fair Learning","year":"2018","author":"besse","key":"ref20"},{"key":"ref22","first-page":"798","article-title":"Link prediction using supervised learning[C]","volume":"30","author":"al","year":"2006","journal-title":"Workshop on Link Analysis Counter-terrorism and Security"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287589"},{"article-title":"The homophily principle in social network analysis[J]","year":"2020","author":"khanam","key":"ref28"},{"journal-title":"Practical Fairness[M]","year":"2020","author":"nielsen","key":"ref27"},{"key":"ref29","first-page":"685","article-title":"Fast computation of Wasserstein barycenters[C]","author":"cuturi","year":"2014","journal-title":"International Conference on Machine Learning"},{"key":"ref8","first-page":"862","article-title":"Wasserstein fair classification[C]","author":"jiang","year":"2020","journal-title":"Uncertainty in Artificial Intelligence"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783311"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441752"},{"key":"ref4","article-title":"On dyadic fairness: Exploring and mitigating bias in graph connections[C]","author":"li","year":"2021","journal-title":"International Conference on Learning Representations"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5429"},{"key":"ref6","first-page":"2357","article-title":"Obtaining fairness using optimal transport theory[C]","author":"gordaliza","year":"2019","journal-title":"International Conference on Machine Learning"},{"key":"ref5","first-page":"1774","article-title":"All of the fairness for edge prediction with optimal transport[C]","author":"laclau","year":"2021","journal-title":"International Conference on Artificial Intelligence and Statistics"}],"event":{"name":"2022 IEEE International Conference on Big Data (Big Data)","start":{"date-parts":[[2022,12,17]]},"location":"Osaka, Japan","end":{"date-parts":[[2022,12,20]]}},"container-title":["2022 IEEE International Conference on Big Data (Big Data)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10020192\/10020156\/10020550.pdf?arnumber=10020550","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,20]],"date-time":"2023-02-20T17:10:45Z","timestamp":1676913045000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10020550\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,17]]},"references-count":29,"URL":"https:\/\/doi.org\/10.1109\/bigdata55660.2022.10020550","relation":{},"subject":[],"published":{"date-parts":[[2022,12,17]]}}}