{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T15:26:55Z","timestamp":1726500415596},"reference-count":126,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,10,17]],"date-time":"2023-10-17T00:00:00Z","timestamp":1697500800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2024,1]]},"DOI":"10.1016\/j.neucom.2023.126948","type":"journal-article","created":{"date-parts":[[2023,10,20]],"date-time":"2023-10-20T07:00:17Z","timestamp":1697785217000},"page":"126948","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["Fair graph representation learning: Empowering NIFTY via Biased Edge Dropout and Fair Attribute Preprocessing"],"prefix":"10.1016","volume":"563","author":[{"ORCID":"http:\/\/orcid.org\/0000-0003-0611-8600","authenticated-orcid":false,"given":"Danilo","family":"Franco","sequence":"first","affiliation":[]},{"given":"Vincenzo Stefano","family":"D\u2019Amato","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-3023-3046","authenticated-orcid":false,"given":"Luca","family":"Pasa","sequence":"additional","affiliation":[]},{"given":"Nicol\u00f2","family":"Navarin","sequence":"additional","affiliation":[]},{"given":"Luca","family":"Oneto","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.neucom.2023.126948_b1","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2022.106874","article-title":"Medical deep learning-a systematic meta-review","author":"Egger","year":"2022","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.neucom.2023.126948_b2","first-page":"33","article-title":"When AIs outperform doctors: confronting the challenges of a tort-induced over-reliance on machine learning","volume":"61","author":"Froomkin","year":"2019","journal-title":"Ariz. L. Rev."},{"issue":"1","key":"10.1016\/j.neucom.2023.126948_b3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-022-05258-z","article-title":"Deep learning-based school attendance prediction for autistic students","volume":"12","author":"Jarbou","year":"2022","journal-title":"Sci. Rep."},{"key":"10.1016\/j.neucom.2023.126948_b4","article-title":"The role of machine learning in cybersecurity","author":"Apruzzese","year":"2022","journal-title":"Digit. Threats: Res. Pract."},{"issue":"4","key":"10.1016\/j.neucom.2023.126948_b5","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1080\/13562517.2020.1748811","article-title":"The datafication of teaching in Higher Education: critical issues and perspectives","volume":"25","author":"Williamson","year":"2020","journal-title":"Teach. High. Educ."},{"issue":"4","key":"10.1016\/j.neucom.2023.126948_b6","doi-asserted-by":"crossref","DOI":"10.14763\/2019.4.1428","article-title":"Datafication","volume":"8","author":"Mejias","year":"2019","journal-title":"Internet Policy Rev."},{"issue":"3","key":"10.1016\/j.neucom.2023.126948_b7","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1038\/s42256-022-00463-x","article-title":"The transformational role of GPU computing and deep learning in drug discovery","volume":"4","author":"Pandey","year":"2022","journal-title":"Nat. Mach. Intell."},{"key":"10.1016\/j.neucom.2023.126948_b8","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.neunet.2020.06.006","article-title":"A gentle introduction to deep learning for graphs","volume":"129","author":"Bacciu","year":"2020","journal-title":"Neural Netw."},{"issue":"1","key":"10.1016\/j.neucom.2023.126948_b9","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","article-title":"A comprehensive survey on graph neural networks","volume":"32","author":"Wu","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.neucom.2023.126948_b10","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.aiopen.2021.01.001","article-title":"Graph neural networks: A review of methods and applications","volume":"1","author":"Zhou","year":"2020","journal-title":"AI Open"},{"key":"10.1016\/j.neucom.2023.126948_b11","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2022.04.072","article-title":"Towards learning trustworthily, automatically, and with guarantees on graphs: An overview","author":"Oneto","year":"2022","journal-title":"Neurocomputing"},{"issue":"5","key":"10.1016\/j.neucom.2023.126948_b12","doi-asserted-by":"crossref","first-page":"1320","DOI":"10.1093\/bioinformatics\/btab830","article-title":"Unsupervised construction of computational graphs for gene expression data with explicit structural inductive biases","volume":"38","author":"Scherer","year":"2022","journal-title":"Bioinformatics"},{"issue":"1","key":"10.1016\/j.neucom.2023.126948_b13","article-title":"Detecting communities using social network analysis in online learning environments: Systematic literature review","volume":"12","author":"Yassine","year":"2022","journal-title":"Wiley Interdiscip. Rev.: Data Min. Knowl. Discov."},{"key":"10.1016\/j.neucom.2023.126948_b14","series-title":"Advances in Structural and Syntactical Pattern Recognition","article-title":"Extended cascade-correlation for syntactic and structural pattern recognition","author":"Sperduti","year":"1996"},{"issue":"3","key":"10.1016\/j.neucom.2023.126948_b15","doi-asserted-by":"crossref","first-page":"714","DOI":"10.1109\/72.572108","article-title":"Supervised neural networks for the classification of structures","volume":"8","author":"Sperduti","year":"1997","journal-title":"IEEE Trans. Neural Netw."},{"key":"10.1016\/j.neucom.2023.126948_b16","doi-asserted-by":"crossref","first-page":"943","DOI":"10.1613\/jair.1.13225","article-title":"Graph kernels: A survey","volume":"72","author":"Nikolentzos","year":"2021","journal-title":"J. Artificial Intelligence Res."},{"key":"10.1016\/j.neucom.2023.126948_b17","series-title":"Neural Information Processing Systems","article-title":"Convolutional networks on graphs for learning molecular fingerprints","author":"Duvenaud","year":"2015"},{"key":"10.1016\/j.neucom.2023.126948_b18","series-title":"Neural Information Processing Systems","article-title":"Convolutional neural networks on graphs with fast localized spectral filtering","author":"Defferrard","year":"2016"},{"year":"2017","series-title":"Graph attention networks","author":"Veli\u010dkovi\u0107","key":"10.1016\/j.neucom.2023.126948_b19"},{"key":"10.1016\/j.neucom.2023.126948_b20","series-title":"International Conference on Machine Learning","article-title":"Simplifying graph convolutional networks","author":"Wu","year":"2019"},{"key":"10.1016\/j.neucom.2023.126948_b21","series-title":"Neural Information Processing Systems","article-title":"Break the ceiling: Stronger multi-scale deep graph convolutional networks","author":"Luan","year":"2019"},{"year":"2020","series-title":"SIGN: Scalable inception graph neural networks","author":"Rossi","key":"10.1016\/j.neucom.2023.126948_b22"},{"key":"10.1016\/j.neucom.2023.126948_b23","series-title":"ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","article-title":"Towards deeper graph neural networks","author":"Liu","year":"2020"},{"key":"10.1016\/j.neucom.2023.126948_b24","first-page":"1","article-title":"Polynomial-based graph convolutional neural networks for graph classification","author":"Pasa","year":"2022","journal-title":"Mach. Learn."},{"key":"10.1016\/j.neucom.2023.126948_b25","series-title":"IEEE International Conference on Data Mining, ICDM","article-title":"Backpropagation-free graph neural networks","author":"Pasa","year":"2022"},{"key":"10.1016\/j.neucom.2023.126948_b26","article-title":"Discriminating systems","author":"West","year":"2019","journal-title":"AI Now"},{"key":"10.1016\/j.neucom.2023.126948_b27","series-title":"CHI Conference on Human Factors in Computing Systems","article-title":"Improving fairness in machine learning systems: What do industry practitioners need?","author":"Holstein","year":"2019"},{"year":"2020","series-title":"Design Justice: Community-Led Practices to Build the Worlds We Need","author":"Costanza-Chock","key":"10.1016\/j.neucom.2023.126948_b28"},{"issue":"6","key":"10.1016\/j.neucom.2023.126948_b29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3419633","article-title":"Implementations in machine ethics: A survey","volume":"53","author":"Tolmeijer","year":"2020","journal-title":"ACM Comput. Surv."},{"issue":"3","key":"10.1016\/j.neucom.2023.126948_b30","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1109\/JPROC.2019.2900622","article-title":"Machine ethics: The design and governance of ethical AI and autonomous systems","volume":"107","author":"Winfield","year":"2019","journal-title":"Proc. IEEE"},{"issue":"2133","key":"10.1016\/j.neucom.2023.126948_b31","doi-asserted-by":"crossref","DOI":"10.1098\/rsta.2018.0085","article-title":"Ethical governance is essential to building trust in robotics and artificial intelligence systems","volume":"376","author":"Winfield","year":"2018","journal-title":"Phil. Trans. R. Soc. A"},{"year":"2017","series-title":"Towards a Code of Ethics for Artificial Intelligence","author":"Boddington","key":"10.1016\/j.neucom.2023.126948_b32"},{"issue":"2","key":"10.1016\/j.neucom.2023.126948_b33","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1080\/09540091.2016.1271400","article-title":"Principles of robotics: regulating robots in the real world","volume":"29","author":"Boden","year":"2017","journal-title":"Connect. Sci."},{"issue":"4","key":"10.1016\/j.neucom.2023.126948_b34","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/MIS.2006.80","article-title":"The nature, importance, and difficulty of machine ethics","volume":"21","author":"Moor","year":"2006","journal-title":"IEEE Intell. Syst."},{"issue":"3","key":"10.1016\/j.neucom.2023.126948_b35","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1080\/09528130050111428","article-title":"Prolegomena to any future artificial moral agent","volume":"12","author":"Allen","year":"2000","journal-title":"J. Exp. Theor. Artif. Intell."},{"issue":"1","key":"10.1016\/j.neucom.2023.126948_b36","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1515\/pjbr-2018-0024","article-title":"GenEth: a general ethical dilemma analyzer","volume":"9","author":"Anderson","year":"2018","journal-title":"Paladyn J. Behav. Robot."},{"issue":"6","key":"10.1016\/j.neucom.2023.126948_b37","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1038\/s42256-019-0055-y","article-title":"Establishing the rules for building trustworthy AI","volume":"1","author":"Floridi","year":"2019","journal-title":"Nat. Mach. Intell."},{"issue":"4","key":"10.1016\/j.neucom.2023.126948_b38","doi-asserted-by":"crossref","first-page":"97","DOI":"10.9785\/cri-2019-200402","article-title":"The EU approach to ethics guidelines for trustworthy artificial intelligence","volume":"20","author":"Smuha","year":"2019","journal-title":"Comput. Law Rev. Int."},{"issue":"4","key":"10.1016\/j.neucom.2023.126948_b39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3419764","article-title":"Bridging the gap between ethics and practice: guidelines for reliable, safe, and trustworthy human-centered AI systems","volume":"10","author":"Shneiderman","year":"2020","journal-title":"ACM Trans. Interact. Intell. Syst."},{"key":"10.1016\/j.neucom.2023.126948_b40","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1007\/s12525-020-00441-4","article-title":"Trustworthy artificial intelligence","volume":"31","author":"Thiebes","year":"2021","journal-title":"Electron. Mark."},{"year":"2023","series-title":"Twitter taught Microsoft\u2019s AI chatbot to be a racist asshole in less than a day","author":"The Verge","key":"10.1016\/j.neucom.2023.126948_b41"},{"year":"2018","series-title":"Algorithms of Oppression","author":"Noble","key":"10.1016\/j.neucom.2023.126948_b42"},{"issue":"5","key":"10.1016\/j.neucom.2023.126948_b43","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1145\/2447976.2447990","article-title":"Discrimination in online ad delivery","volume":"56","author":"Sweeney","year":"2013","journal-title":"Commun. ACM"},{"key":"10.1016\/j.neucom.2023.126948_b44","series-title":"AAAI\/ACM Conference on AI, Ethics, and Society","article-title":"Algorithms that\u201d don\u2019t see color\u201d measuring biases in lookalike and special ad audiences","author":"Sapiezynski","year":"2022"},{"key":"10.1016\/j.neucom.2023.126948_b45","series-title":"Conference on Fairness, Accountability and Transparency","article-title":"Gender shades: Intersectional accuracy disparities in commercial gender classification","author":"Buolamwini","year":"2018"},{"year":"2020","series-title":"Portland passes broadest facial recognition ban in the US","author":"Metz","key":"10.1016\/j.neucom.2023.126948_b46"},{"issue":"6","key":"10.1016\/j.neucom.2023.126948_b47","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3457607","article-title":"A survey on bias and fairness in machine learning","volume":"54","author":"Mehrabi","year":"2021","journal-title":"ACM Comput. Surv."},{"year":"2020","series-title":"Fairness in machine learning: A survey","author":"Caton","key":"10.1016\/j.neucom.2023.126948_b48"},{"key":"10.1016\/j.neucom.2023.126948_b49","series-title":"IEEE\/ACM International Workshop on Software Fairness","article-title":"Fairness definitions explained","author":"Verma","year":"2018"},{"key":"10.1016\/j.neucom.2023.126948_b50","first-page":"1","article-title":"The statistical fairness field guide: perspectives from social and formal sciences","author":"Carey","year":"2022","journal-title":"AI Ethics"},{"key":"10.1016\/j.neucom.2023.126948_b51","series-title":"Recent Trends in Learning from Data","article-title":"Fairness in machine learning","author":"Oneto","year":"2020"},{"key":"10.1016\/j.neucom.2023.126948_b52","series-title":"IEEE International Conference on Data Mining Workshops","article-title":"Building classifiers with independency constraints","author":"Calders","year":"2009"},{"key":"10.1016\/j.neucom.2023.126948_b53","series-title":"Neural Information Processing Systems","article-title":"Equality of opportunity in supervised learning","author":"Hardt","year":"2016"},{"key":"10.1016\/j.neucom.2023.126948_b54","series-title":"Neural Information Processing Systems","article-title":"Counterfactual fairness","author":"Kusner","year":"2017"},{"key":"10.1016\/j.neucom.2023.126948_b55","doi-asserted-by":"crossref","DOI":"10.1111\/bjet.13217","article-title":"How do the existing fairness metrics and unfairness mitigation algorithms contribute to ethical learning analytics?","author":"Deho","year":"2022","journal-title":"Br. J. Educ. Technol."},{"key":"10.1016\/j.neucom.2023.126948_b56","series-title":"ACM International Conference on Information & Knowledge Management","article-title":"Fair graph mining","author":"Kang","year":"2021"},{"key":"10.1016\/j.neucom.2023.126948_b57","article-title":"Auditing network embedding: An edge influence based approach","author":"Wang","year":"2021","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.neucom.2023.126948_b58","series-title":"International Conference on Machine Learning","article-title":"Neural message passing for quantum chemistry","author":"Gilmer","year":"2017"},{"issue":"2","key":"10.1016\/j.neucom.2023.126948_b59","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1109\/TAI.2021.3076021","article-title":"Graph learning: A survey","volume":"2","author":"Xia","year":"2021","journal-title":"IEEE Trans. Artif. Intell."},{"issue":"1","key":"10.1016\/j.neucom.2023.126948_b60","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40649-019-0069-y","article-title":"Graph convolutional networks: a comprehensive review","volume":"6","author":"Zhang","year":"2019","journal-title":"Comput. Soc. Netw."},{"key":"10.1016\/j.neucom.2023.126948_b61","series-title":"International Conference on Learning Representations","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2017"},{"key":"10.1016\/j.neucom.2023.126948_b62","series-title":"International Conference on Machine Learning","article-title":"Learning fair representations","author":"Zemel","year":"2013"},{"key":"10.1016\/j.neucom.2023.126948_b63","series-title":"Advances in Neural Information Processing Systems","article-title":"Inherent tradeoffs in learning fair representations","author":"Zhao","year":"2019"},{"key":"10.1016\/j.neucom.2023.126948_b64","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1016\/j.neucom.2021.05.109","article-title":"Deep fair models for complex data: Graphs labeling and explainable face recognition","volume":"470","author":"Franco","year":"2022","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2023.126948_b65","series-title":"IEEE International Conference on Acoustics, Speech and Signal Processing","article-title":"Bias mitigation post-processing for individual and group fairness","author":"Lohia","year":"2019"},{"key":"10.1016\/j.neucom.2023.126948_b66","article-title":"In-processing modeling techniques for machine learning fairness: A survey","author":"Wan","year":"2022","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"10.1016\/j.neucom.2023.126948_b67","series-title":"AAAI Conference on Web and Social Media","article-title":"Fair representation learning for heterogeneous information networks","author":"Zeng","year":"2021"},{"year":"2022","series-title":"Fair node representation learning via adaptive data augmentation","author":"Kose","key":"10.1016\/j.neucom.2023.126948_b68"},{"year":"2022","series-title":"A survey on fairness for machine learning on graphs","author":"Choudhary","key":"10.1016\/j.neucom.2023.126948_b69"},{"key":"10.1016\/j.neucom.2023.126948_b70","series-title":"European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning","article-title":"Learning deep fair graph neural networks","author":"Navarin","year":"2020"},{"year":"2022","series-title":"Fairness in graph mining: A survey","author":"Dong","key":"10.1016\/j.neucom.2023.126948_b71"},{"key":"10.1016\/j.neucom.2023.126948_b72","series-title":"International Joint Conference on Artificial Intelligence","article-title":"Fairwalk: Towards fair graph embedding","author":"Rahman","year":"2019"},{"key":"10.1016\/j.neucom.2023.126948_b73","series-title":"AAAI Conference on Artificial Intelligence","article-title":"Crosswalk: Fairness-enhanced node representation learning","author":"Khajehnejad","year":"2022"},{"issue":"4","key":"10.1016\/j.neucom.2023.126948_b74","doi-asserted-by":"crossref","first-page":"2853","DOI":"10.1007\/s10878-021-00788-0","article-title":"HM-EIICT: Fairness-aware link prediction in complex networks using community information","volume":"44","author":"Saxena","year":"2022","journal-title":"J. Comb. Optim."},{"key":"10.1016\/j.neucom.2023.126948_b75","series-title":"ACM Web Conference","article-title":"Fairness-aware pagerank","author":"Tsioutsiouliklis","year":"2021"},{"year":"2022","series-title":"Fairmod: Fair link prediction and recommendation via graph modification","author":"Current","key":"10.1016\/j.neucom.2023.126948_b76"},{"issue":"3","key":"10.1016\/j.neucom.2023.126948_b77","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1109\/TAI.2021.3133818","article-title":"Fairdrop: Biased edge dropout for enhancing fairness in graph representation learning","volume":"3","author":"Spinelli","year":"2021","journal-title":"IEEE Trans. Artif. Intell."},{"key":"10.1016\/j.neucom.2023.126948_b78","series-title":"SIAM International Conference on Data Mining","article-title":"On the information unfairness of social networks","author":"Jalali","year":"2020"},{"key":"10.1016\/j.neucom.2023.126948_b79","series-title":"International Conference on Learning Representations","article-title":"On dyadic fairness: Exploring and mitigating bias in graph connections","author":"Li","year":"2021"},{"key":"10.1016\/j.neucom.2023.126948_b80","series-title":"ACM Web Conference","article-title":"Edits: Modeling and mitigating data bias for graph neural networks","author":"Dong","year":"2022"},{"key":"10.1016\/j.neucom.2023.126948_b81","series-title":"Advances in Neural Information Processing Systems","article-title":"Beyond parity: Fairness objectives for collaborative filtering","author":"Yao","year":"2017"},{"key":"10.1016\/j.neucom.2023.126948_b82","series-title":"International Conference on Machine Learning","article-title":"Guarantees for spectral clustering with fairness constraints","author":"Kleindessner","year":"2019"},{"key":"10.1016\/j.neucom.2023.126948_b83","series-title":"Machine Learning and Knowledge Discovery in Databases","article-title":"The kl-divergence between a graph model and its fair i-projection as a fairness regularizer","author":"Buyl","year":"2021"},{"key":"10.1016\/j.neucom.2023.126948_b84","series-title":"ACM SIGKDD Conference on Knowledge Discovery & Data Mining","article-title":"Inform: Individual fairness on graph mining","author":"Kang","year":"2020"},{"key":"10.1016\/j.neucom.2023.126948_b85","series-title":"Uncertainty in Artificial Intelligence","article-title":"Towards a unified framework for fair and stable graph representation learning","author":"Agarwal","year":"2021"},{"key":"10.1016\/j.neucom.2023.126948_b86","series-title":"ACM SIGKDD Conference on Knowledge Discovery & Data Mining","article-title":"Individual fairness for graph neural networks: A ranking based approach","author":"Dong","year":"2021"},{"key":"10.1016\/j.neucom.2023.126948_b87","series-title":"International Conference on Machine Learning","article-title":"Compositional fairness constraints for graph embeddings","author":"Bose","year":"2019"},{"key":"10.1016\/j.neucom.2023.126948_b88","series-title":"International Joint Conference on Artificial Intelligence","article-title":"Adversarial graph embeddings for fair influence maximization over social networks","author":"Khajehnejad","year":"2020"},{"key":"10.1016\/j.neucom.2023.126948_b89","series-title":"AAAI Conference on Artificial Intelligence","article-title":"Fairness-aware news recommendation with decomposed adversarial learning","author":"Wu","year":"2021"},{"key":"10.1016\/j.neucom.2023.126948_b90","series-title":"ACM International Conference on Web Search and Data Mining","article-title":"Say no to the discrimination: Learning fair graph neural networks with limited sensitive attribute information","author":"Dai","year":"2021"},{"key":"10.1016\/j.neucom.2023.126948_b91","series-title":"ACM Web Conference","article-title":"Learning fair representations for recommendation: A graph-based perspective","author":"Wu","year":"2021"},{"issue":"11","key":"10.1016\/j.neucom.2023.126948_b92","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3422622","article-title":"Generative adversarial networks","volume":"63","author":"Goodfellow","year":"2020","journal-title":"Commun. ACM"},{"key":"10.1016\/j.neucom.2023.126948_b93","series-title":"IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining","article-title":"Debiasing graph representations via metadata-orthogonal training","author":"Palowitch","year":"2020"},{"year":"2017","series-title":"UCI machine learning repository","author":"Dua","key":"10.1016\/j.neucom.2023.126948_b94"},{"issue":"2","key":"10.1016\/j.neucom.2023.126948_b95","doi-asserted-by":"crossref","first-page":"2473","DOI":"10.1016\/j.eswa.2007.12.020","article-title":"The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients","volume":"36","author":"Yeh","year":"2009","journal-title":"Expert Syst. Appl."},{"issue":"3","key":"10.1016\/j.neucom.2023.126948_b96","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1080\/15377938.2014.984045","article-title":"The effect of race\/ethnicity on sentencing: Examining sentence type, jail length, and prison length","volume":"13","author":"Jordan","year":"2015","journal-title":"Ethn. Crim. Justice"},{"key":"10.1016\/j.neucom.2023.126948_b97","series-title":"International Scientific Conference and International Workshop Present Day Trends of Innovations","article-title":"Data analysis in public social networks","author":"Takac","year":"2012"},{"key":"10.1016\/j.neucom.2023.126948_b98","series-title":"Neural Information Processing Systems","article-title":"Learning to discover social circles in ego networks","author":"Leskovec","year":"2012"},{"year":"2014","series-title":"Understanding Machine Learning: From Theory to Algorithms","author":"Shalev-Shwartz","key":"10.1016\/j.neucom.2023.126948_b99"},{"key":"10.1016\/j.neucom.2023.126948_b100","series-title":"Neural Information Processing Systems","article-title":"Empirical risk minimization under fairness constraints","author":"Donini","year":"2018"},{"issue":"3","key":"10.1016\/j.neucom.2023.126948_b101","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1109\/TNN.2008.2010350","article-title":"Neural network for graphs: A contextual constructive approach","volume":"20","author":"Micheli","year":"2009","journal-title":"IEEE Trans. Neural Netw."},{"issue":"1","key":"10.1016\/j.neucom.2023.126948_b102","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","article-title":"The graph neural network model","volume":"20","author":"Scarselli","year":"2008","journal-title":"IEEE Trans. Neural Netw."},{"key":"10.1016\/j.neucom.2023.126948_b103","series-title":"Neural Information Processing Systems","article-title":"Inductive representation learning on large graphs","author":"Hamilton","year":"2017"},{"key":"10.1016\/j.neucom.2023.126948_b104","series-title":"AAAI Conference on Artificial Intelligence","article-title":"Weisfeiler and leman go neural: Higher-order graph neural networks","author":"Morris","year":"2019"},{"key":"10.1016\/j.neucom.2023.126948_b105","series-title":"International Conference on Learning Representations","article-title":"Gated graph sequence neural networks","author":"Li","year":"2016"},{"key":"10.1016\/j.neucom.2023.126948_b106","series-title":"International Conference on Learning Representations","article-title":"Predict then propagate: Graph neural networks meet personalized pagerank","author":"Gasteiger","year":"2019"},{"key":"10.1016\/j.neucom.2023.126948_b107","series-title":"International Conference on Machine Learning","article-title":"Simple and deep graph convolutional networks","author":"Chen","year":"2020"},{"key":"10.1016\/j.neucom.2023.126948_b108","series-title":"Conference on Fairness, Accountability and Transparency","article-title":"Decoupled classifiers for group-fair and efficient machine learning","author":"Dwork","year":"2018"},{"key":"10.1016\/j.neucom.2023.126948_b109","series-title":"Neural Information Processing Systems","article-title":"Signature verification using a\u201d siamese\u201d time delay neural network","author":"Bromley","year":"1993"},{"key":"10.1016\/j.neucom.2023.126948_b110","series-title":"Innovations in Theoretical Computer Science Conference","article-title":"Fairness through awareness","author":"Dwork","year":"2012"},{"key":"10.1016\/j.neucom.2023.126948_b111","series-title":"International Conference on Learning Representations","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2015"},{"key":"10.1016\/j.neucom.2023.126948_b112","series-title":"International Conference on Learning Representations","article-title":"The variational fair autoencoder","author":"Louizos","year":"2016"},{"key":"10.1016\/j.neucom.2023.126948_b113","series-title":"Neural Information Processing Systems","article-title":"Exploiting mmd and sinkhorn divergences for fair and transferable representation learning","author":"Oneto","year":"2020"},{"key":"10.1016\/j.neucom.2023.126948_b114","series-title":"International Conference on Machine Learning","article-title":"Flexibly fair representation learning by disentanglement","author":"Creager","year":"2019"},{"key":"10.1016\/j.neucom.2023.126948_b115","series-title":"AAAI\/ACM Conference on AI, Ethics, and Society","article-title":"Uncovering and mitigating algorithmic bias through learned latent structure","author":"Amini","year":"2019"},{"key":"10.1016\/j.neucom.2023.126948_b116","series-title":"Neural Information Processing Systems","article-title":"Invariant representations without adversarial training","author":"Moyer","year":"2018"},{"key":"10.1016\/j.neucom.2023.126948_b117","first-page":"1","article-title":"Error metrics and performance fitness indicators for artificial intelligence and machine learning in engineering and sciences","author":"Naser","year":"2021","journal-title":"Archit. Struct. Constr."},{"year":"2011","series-title":"The Filter Bubble: What the Internet is Hiding from You","author":"Pariser","key":"10.1016\/j.neucom.2023.126948_b118"},{"key":"10.1016\/j.neucom.2023.126948_b119","series-title":"International Conference on World Wide Web","article-title":"Exploring the filter bubble: the effect of using recommender systems on content diversity","author":"Nguyen","year":"2014"},{"key":"10.1016\/j.neucom.2023.126948_b120","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1007\/s10676-015-9380-y","article-title":"Breaking the filter bubble: democracy and design","volume":"17","author":"Bozdag","year":"2015","journal-title":"Ethics Inf. Technol."},{"key":"10.1016\/j.neucom.2023.126948_b121","doi-asserted-by":"crossref","DOI":"10.1109\/TKDE.2023.3265598","article-title":"Fairness in graph mining: A survey","author":"Dong","year":"2023","journal-title":"IEEE Trans. Knowl. Data Eng."},{"year":"2022","series-title":"A comprehensive survey on trustworthy graph neural networks: Privacy, robustness, fairness, and explainability","author":"Dai","key":"10.1016\/j.neucom.2023.126948_b122"},{"key":"10.1016\/j.neucom.2023.126948_b123","series-title":"Neural Information Processing Systems","article-title":"Pytorch: An imperative style, high-performance deep learning library","author":"Paszke","year":"2019"},{"key":"10.1016\/j.neucom.2023.126948_b124","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1146\/annurev-statistics-042720-125902","article-title":"Algorithmic fairness: Choices, assumptions, and definitions","volume":"8","author":"Mitchell","year":"2021","journal-title":"Annu. Rev. Stat. Appl."},{"year":"2020","series-title":"Algorithmic fairness","author":"Pessach","key":"10.1016\/j.neucom.2023.126948_b125"},{"key":"10.1016\/j.neucom.2023.126948_b126","series-title":"8th International Conference on Learning Representations, ICLR","article-title":"A fair comparison of graph neural networks for graph classification","author":"Errica","year":"2020"}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231223010718?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231223010718?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2023,11,6]],"date-time":"2023-11-06T15:09:05Z","timestamp":1699283345000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231223010718"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1]]},"references-count":126,"alternative-id":["S0925231223010718"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2023.126948","relation":{},"ISSN":["0925-2312"],"issn-type":[{"type":"print","value":"0925-2312"}],"subject":[],"published":{"date-parts":[[2024,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Fair graph representation learning: Empowering NIFTY via Biased Edge Dropout and Fair Attribute Preprocessing","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2023.126948","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2023 The Author(s). Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"126948"}}