{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T07:20:33Z","timestamp":1773040833078,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":45,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,6,3]],"date-time":"2024-06-03T00:00:00Z","timestamp":1717372800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100006374","name":"HORIZON EUROPE Framework Programme","doi-asserted-by":"publisher","award":["101057746"],"award-info":[{"award-number":["101057746"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,6,3]]},"DOI":"10.1145\/3630106.3658919","type":"proceedings-article","created":{"date-parts":[[2024,6,5]],"date-time":"2024-06-05T09:14:21Z","timestamp":1717578861000},"page":"467-479","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Value is in the Eye of the Beholder: A Framework for an Equitable Graph Data Evaluation"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-2936-1297","authenticated-orcid":false,"given":"Francesco Paolo","family":"Nerini","sequence":"first","affiliation":[{"name":"DIAG, Sapienza University of Rome, Italy and CENTAI Institute, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8865-4495","authenticated-orcid":false,"given":"Paolo","family":"Bajardi","sequence":"additional","affiliation":[{"name":"CENTAI Institute, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3336-0374","authenticated-orcid":false,"given":"Andr\u00e9","family":"Panisson","sequence":"additional","affiliation":[{"name":"CENTAI Institute, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,6,5]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3328526.3329589"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfs.2017.05.012"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3572751.3572755"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3565011.3569054"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","unstructured":"Santiago\u00a0Andr\u00e9s Azcoitia Marius Paraschiv and Nikolaos Laoutaris. 2020. Computing the Relative Value of Spatio-Temporal Data in Wholesale and Retail Data Marketplaces. (2020). https:\/\/doi.org\/10.48550\/arXiv.2002.11193 arXiv:arXiv:2002.11193","DOI":"10.48550\/arXiv.2002.11193"},{"key":"e_1_3_2_1_6_1","volume-title":"Proceedings of the 23rd Conference on Learning Theory","author":"Cesa\u00a0Bianchi N","year":"2010","unstructured":"N Cesa\u00a0Bianchi, Claudio Gentile, F Vitale, G Zappella, 2010. Active learning on trees and graphs. In Proceedings of the 23rd Conference on Learning Theory (Haifa, Israel). Omnipress, 320\u2013332."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2021.3125565"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441752"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3265598"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3575637.3575644"},{"key":"e_1_3_2_1_11_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning","author":"Ghorbani Amirata","year":"2019","unstructured":"Amirata Ghorbani and James Zou. 2019. Data Shapley: Equitable Valuation of Data for Machine Learning. In Proceedings of the 36th International Conference on Machine Learning (Long Beach, California, USA) (Proceedings of Machine Learning Research, Vol.\u00a097), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). PMLR, 2242\u20132251. https:\/\/proceedings.mlr.press\/v97\/ghorbani19c.html"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s001820050125"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3531146.3533085"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_3_2_1_15_1","volume-title":"Advances in Neural Information Processing Systems (Long Beach, California, USA) (NIPS","author":"Hamilton Will","year":"2017","unstructured":"Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems (Long Beach, California, USA) (NIPS 2017, Vol.\u00a030), I.\u00a0Guyon, U.\u00a0Von Luxburg, S.\u00a0Bengio, H.\u00a0Wallach, R.\u00a0Fergus, S.\u00a0Vishwanathan, and R.\u00a0Garnett (Eds.). Curran Associates, Inc.https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2017\/file\/5dd9db5e033da9c6fb5ba83c7a7ebea9-Paper.pdf"},{"key":"e_1_3_2_1_16_1","volume-title":"Graph representation learning","author":"Hamilton L","unstructured":"William\u00a0L Hamilton. 2020. Graph representation learning. Morgan & Claypool Publishers."},{"key":"e_1_3_2_1_17_1","volume-title":"Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics","author":"Jia Ruoxi","year":"2019","unstructured":"Ruoxi Jia, David Dao, Boxin Wang, Frances\u00a0Ann Hubis, Nick Hynes, Nezihe\u00a0Merve G\u00fcrel, Bo Li, Ce Zhang, Dawn Song, and Costas\u00a0J. Spanos. 2019. Towards Efficient Data Valuation Based on the Shapley Value. In Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics (Valencia, Spain) (Proceedings of Machine Learning Research, Vol.\u00a089), Kamalika Chaudhuri and Masashi Sugiyama (Eds.). PMLR, 1167\u20131176. https:\/\/proceedings.mlr.press\/v89\/jia19a.html"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","unstructured":"Thomas\u00a0N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. (2016). https:\/\/doi.org\/10.48550\/arXiv.1609.02907 arXiv:arXiv:1609.02907","DOI":"10.48550\/arXiv.1609.02907"},{"key":"e_1_3_2_1_19_1","unstructured":"Harold\u00a0William Kuhn and Albert\u00a0William Tucker (Eds.). 1953. Contributions to the Theory of Games. Annals of Mathematics Studies Vol.\u00a02. Princeton University Press. 307\u2013317 pages. https:\/\/books.google.it\/books?id=EWCYDwAAQBAJ"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3501811"},{"key":"e_1_3_2_1_21_1","volume-title":"Probability on trees and networks. Vol.\u00a042","author":"Lyons Russell","unstructured":"Russell Lyons and Yuval Peres. 2017. Probability on trees and networks. Vol.\u00a042. Cambridge University Press."},{"key":"e_1_3_2_1_22_1","volume-title":"Birds of a feather: Homophily in social networks. Annual review of sociology 27, 1","author":"McPherson Miller","year":"2001","unstructured":"Miller McPherson, Lynn Smith-Lovin, and James\u00a0M Cook. 2001. Birds of a feather: Homophily in social networks. Annual review of sociology 27, 1 (2001), 415\u2013444."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.89.208701"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","unstructured":"Marius Paraschiv and Nikolaos Laoutaris. 2019. Valuating User Data in a Human-Centric Data Economy. (2019). https:\/\/doi.org\/10.48550\/arXiv.1909.01137 arXiv:arXiv:1909.01137","DOI":"10.48550\/arXiv.1909.01137"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.3045927"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3188728"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","unstructured":"Mohammad Rasouli and Michael\u00a0I Jordan. 2021. Data sharing markets. (2021). https:\/\/doi.org\/10.48550\/arXiv.2107.08630 arXiv:arXiv:2107.08630","DOI":"10.48550\/arXiv.2107.08630"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/TST.2016.7590317"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.47363\/JAICC\/2023"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.5555\/2615731.2615766"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/BigData47090.2019.9006327"},{"key":"e_1_3_2_1_32_1","volume-title":"Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track (Virtual Event), Yuxiao Dong, Nicolas Kourtellis, Barbara Hammer, and Jose\u00a0A","author":"Starnini Michele","unstructured":"Michele Starnini, Charalampos\u00a0E. Tsourakakis, Maryam Zamanipour, Andr\u00e9 Panisson, Walter Allasia, Marco Fornasiero, Laura\u00a0Li Puma, Valeria Ricci, Silvia Ronchiadin, Angela Ugrinoska, Marco Varetto, and Dario Moncalvo. 2021. Smurf-Based Anti-money Laundering in Time-Evolving Transaction Networks. In Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track (Virtual Event), Yuxiao Dong, Nicolas Kourtellis, Barbara Hammer, and Jose\u00a0A. Lozano (Eds.). Springer International Publishing, Cham, 171\u2013186."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","unstructured":"Toyotaro Suzumura Yi Zhou Natahalie Baracaldo Guangnan Ye Keith Houck Ryo Kawahara Ali Anwar Lucia\u00a0Larise Stavarache Yuji Watanabe Pablo Loyola Daniel Klyashtorny Heiko Ludwig and Kumar Bhaskaran. 2019. Towards Federated Graph Learning for Collaborative Financial Crimes Detection. (2019). https:\/\/doi.org\/10.48550\/arXiv.1909.12946 arXiv:arXiv:1909.12946","DOI":"10.48550\/arXiv.1909.12946"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","unstructured":"Mark Weber Jie Chen Toyotaro Suzumura Aldo Pareja Tengfei Ma Hiroki Kanezashi Tim Kaler Charles\u00a0E. Leiserson and Tao\u00a0B. Schardl. 2018. Scalable Graph Learning for Anti-Money Laundering: A First Look. (2018). https:\/\/doi.org\/10.48550\/arXiv.1812.00076 arXiv:arXiv:1812.00076","DOI":"10.48550\/arXiv.1812.00076"},{"key":"e_1_3_2_1_35_1","volume-title":"Proceedings of The 25th International Conference on Artificial Intelligence and Statistics(Proceedings of Machine Learning Research, Vol.\u00a0151)","author":"Xu Xinlei","year":"2022","unstructured":"Xinlei Xu, Awni Hannun, and Laurens Van Der\u00a0Maaten. 2022. Data Appraisal Without Data Sharing. In Proceedings of The 25th International Conference on Artificial Intelligence and Statistics(Proceedings of Machine Learning Research, Vol.\u00a0151), Gustau Camps-Valls, Francisco J.\u00a0R. Ruiz, and Isabel Valera (Eds.). PMLR, 11422\u201311437. https:\/\/proceedings.mlr.press\/v151\/xu22e.html"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/5964068"},{"key":"e_1_3_2_1_37_1","volume-title":"Proceedings of The 33rd International Conference on Machine Learning","author":"Yang Zhilin","year":"2016","unstructured":"Zhilin Yang, William Cohen, and Ruslan Salakhudinov. 2016. Revisiting Semi-Supervised Learning with Graph Embeddings. In Proceedings of The 33rd International Conference on Machine Learning (New York City, New York, USA) (Proceedings of Machine Learning Research, Vol.\u00a048), Maria\u00a0Florina Balcan and Kilian\u00a0Q. Weinberger (Eds.). PMLR, 40\u201348. https:\/\/proceedings.mlr.press\/v48\/yanga16.html"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2110.12906"},{"key":"e_1_3_2_1_39_1","volume-title":"Advances in Neural Information Processing Systems (Virtual Event) (NeurIPS","author":"Carl Yang Ke ZHANG","year":"2021","unstructured":"Ke ZHANG, Carl Yang, Xiaoxiao Li, Lichao Sun, and Siu\u00a0Ming Yiu. 2021. Subgraph Federated Learning with Missing Neighbor Generation. In Advances in Neural Information Processing Systems (Virtual Event) (NeurIPS 2021, Vol.\u00a034), M.\u00a0Ranzato, A.\u00a0Beygelzimer, Y.\u00a0Dauphin, P.S. Liang, and J.\u00a0Wortman Vaughan (Eds.). Curran Associates, Inc., 6671\u20136682. https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2021\/file\/34adeb8e3242824038aa65460a47c29e-Paper.pdf"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","unstructured":"Wenbin Zhang Shimei Pan Shuigeng Zhou Toby Walsh and Jeremy\u00a0C. Weiss. 2023. Fairness Amidst Non-IID Graph Data: Current Achievements and Future Directions. (2023). https:\/\/doi.org\/10.48550\/arXiv.2202.07170 arXiv:arXiv:2202.07170","DOI":"10.48550\/arXiv.2202.07170"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3543873.3587681"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.14778\/3587136.3587141"},{"key":"e_1_3_2_1_43_1","volume-title":"Advances in Neural Information Processing Systems (Virtual Event) (NeurIPS","author":"Zhu Jiong","year":"2020","unstructured":"Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, and Danai Koutra. 2020. Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs. In Advances in Neural Information Processing Systems (Virtual Event) (NeurIPS 2020, Vol.\u00a033), H.\u00a0Larochelle, M.\u00a0Ranzato, R.\u00a0Hadsell, M.F. Balcan, and H.\u00a0Lin (Eds.). Curran Associates, Inc., 7793\u20137804. https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2020\/file\/58ae23d878a47004366189884c2f8440-Paper.pdf"},{"key":"e_1_3_2_1_44_1","volume-title":"Learning from Labeled and Unlabeled Data with Label Propagation","author":"Zhu Xiaojin","unstructured":"Xiaojin Zhu and Zoubin Ghahramani. 2002. Learning from Labeled and Unlabeled Data with Label Propagation. Technical Report. Pittsburgh, PA, USA. CMU-CALD-02-107."},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/3593013.3594029"}],"event":{"name":"FAccT '24: The 2024 ACM Conference on Fairness, Accountability, and Transparency","location":"Rio de Janeiro Brazil","acronym":"FAccT '24"},"container-title":["The 2024 ACM Conference on Fairness Accountability and Transparency"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3630106.3658919","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3630106.3658919","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T17:31:35Z","timestamp":1755883895000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3630106.3658919"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,3]]},"references-count":45,"alternative-id":["10.1145\/3630106.3658919","10.1145\/3630106"],"URL":"https:\/\/doi.org\/10.1145\/3630106.3658919","relation":{},"subject":[],"published":{"date-parts":[[2024,6,3]]},"assertion":[{"value":"2024-06-05","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}