{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T09:57:53Z","timestamp":1770544673746,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":42,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T00:00:00Z","timestamp":1724457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,8,25]]},"DOI":"10.1145\/3637528.3671831","type":"proceedings-article","created":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T04:54:55Z","timestamp":1724561695000},"page":"2420-2431","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Unifying Evolution, Explanation, and Discernment: A Generative Approach for Dynamic Graph Counterfactuals"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2991-2279","authenticated-orcid":false,"given":"Bardh","family":"Prenkaj","sequence":"first","affiliation":[{"name":"Technical University of Munich, Munich, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-0754-1742","authenticated-orcid":false,"given":"Mario","family":"Villaiz\u00e1n-Vallelado","sequence":"additional","affiliation":[{"name":"University of Valladolid & Telef\u00f3nica Research and Development, Valladolid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9333-228X","authenticated-orcid":false,"given":"Tobias","family":"Leemann","sequence":"additional","affiliation":[{"name":"University of T\u00fcbingen, T\u00fcbingen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3123-7268","authenticated-orcid":false,"given":"Gjergji","family":"Kasneci","sequence":"additional","affiliation":[{"name":"Technical University of Munich, Munich, Germany"}]}],"member":"320","published-online":{"date-parts":[[2024,8,24]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467154"},{"key":"e_1_3_2_2_2_1","volume-title":"9th International Conference on Learning Representations, ICLR 2021","author":"Alon Uri","year":"2021","unstructured":"Uri Alon and Eran Yahav. 2021. On the Bottleneck of Graph Neural Networks and its Practical Implications. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3--7, 2021. OpenReview.net."},{"key":"e_1_3_2_2_3_1","first-page":"7828","article-title":"Training normalizing flows with the information bottleneck for competitive generative classification","volume":"33","author":"Ardizzone Lynton","year":"2020","unstructured":"Lynton Ardizzone, Radek Mackowiak, Carsten Rother, and Ullrich K\u00f6the. 2020. Training normalizing flows with the information bottleneck for competitive generative classification. Advances in Neural Information Processing Systems, Vol. 33 (2020), 7828--7840.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_4_1","first-page":"5644","article-title":"Robust counterfactual explanations on graph neural networks","volume":"34","author":"Bajaj Mohit","year":"2021","unstructured":"Mohit Bajaj, Lingyang Chu, Zi Yu Xue, Jian Pei, Lanjun Wang, Peter Cho-Ho Lam, and Yong Zhang. 2021. Robust counterfactual explanations on graph neural networks. Advances in Neural Information Processing Systems, Vol. 34 (2021), 5644--5655.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1800683115"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3358009"},{"key":"e_1_3_2_2_7_1","volume-title":"Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608","author":"Doshi-Velez Finale","year":"2017","unstructured":"Finale Doshi-Velez and Been Kim. 2017. Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608 (2017)."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411903"},{"key":"e_1_3_2_2_9_1","volume-title":"Official Journal of the European Union","author":"European Union. 2016. Regulation (EU) 2016\/679 of the European Parliament and of the Council","year":"2016","unstructured":"European Union. 2016. Regulation (EU) 2016\/679 of the European Parliament and of the Council. Official Journal of the European Union (2016)."},{"key":"e_1_3_2_2_10_1","volume-title":"Laying Down Harmonised Rules on Artificial Intelligence","author":"Union European","year":"2023","unstructured":"European Union. 2023. Laying Down Harmonised Rules on Artificial Intelligence (Artificial Intelligence Act) and Amending Certain Union Legislative Acts. Official Journal of the European Union (2023)."},{"key":"e_1_3_2_2_11_1","volume-title":"Proc. of the 37th Graph Repr. Learning and Beyond Workshop at ICML 2020. Int. Conf. on Machine Learning, 28","author":"Faber L.","unstructured":"L. Faber, A. K. Moghaddam, and R. Wattenhofer. 2020. Contrastive Graph Neural Network Explanation. In Proc. of the 37th Graph Repr. Learning and Beyond Workshop at ICML 2020. Int. Conf. on Machine Learning, 28."},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380079"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"crossref","unstructured":"Wenqi Fan Yao Ma Qing Li Yuan He Eric Zhao Jiliang Tang and Dawei Yin. 2019. Graph neural networks for social recommendation. In The world wide web conference. 417--426.","DOI":"10.1145\/3308558.3313488"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR48806.2021.9412499"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539321"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1561\/9781680836233"},{"key":"e_1_3_2_2_17_1","volume-title":"NeurIPS Workshop on Bayesian Deep Learning.","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling. 2016. Variational graph auto-encoders. In NeurIPS Workshop on Bayesian Deep Learning."},{"key":"e_1_3_2_2_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 Proc. OpenReview.net."},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3159652.3159729"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1142\/S1793351X20300022"},{"key":"e_1_3_2_2_21_1","volume-title":"International Conference on Machine Learning. PMLR, 3804--3814","author":"Li Yingzhen","year":"2019","unstructured":"Yingzhen Li, John Bradshaw, and Yash Sharma. 2019. Are generative classifiers more robust to adversarial attacks?. In International Conference on Machine Learning. PMLR, 3804--3814."},{"key":"e_1_3_2_2_22_1","volume-title":"ICML 2022 2nd AI for Science Workshop.","author":"Li Zelong","year":"2022","unstructured":"Zelong Li, Jianchao Ji, and Yongfeng Zhang. 2022. From Kepler to Newton: Explainable AI for Science Discovery. In ICML 2022 2nd AI for Science Workshop."},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10044-012-0284-8"},{"key":"e_1_3_2_2_24_1","volume-title":"CLEAR: Generative Counterfactual Explanations on Graphs. In Advances in Neural Information Processing Systems, Alice H. Oh, Alekh Agarwal, Danielle Belgrave, and Kyunghyun Cho (Eds.).","author":"Ma Jing","year":"2022","unstructured":"Jing Ma, Ruocheng Guo, Saumitra Mishra, Aidong Zhang, and Jundong Li. 2022. CLEAR: Generative Counterfactual Explanations on Graphs. In Advances in Neural Information Processing Systems, Alice H. Oh, Alekh Agarwal, Danielle Belgrave, and Kyunghyun Cho (Eds.)."},{"key":"e_1_3_2_2_25_1","volume-title":"Proc. of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence, UAI 2022","volume":"180","author":"Nemirovsky Daniel","year":"2022","unstructured":"Daniel Nemirovsky, Nicolas Thiebaut, Ye Xu, and Abhishek Gupta. 2022. CounteRGAN: Generating counterfactuals for real-time recourse and interpretability using residual GANs. In Proc. of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence, UAI 2022, 1--5 August 2022, Eindhoven, The Netherlands, Vol. 180. PMLR, 1488--1497."},{"key":"e_1_3_2_2_26_1","volume-title":"On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes. Advances in neural information processing systems","author":"Ng Andrew","year":"2001","unstructured":"Andrew Ng and Michael Jordan. 2001. On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes. Advances in neural information processing systems, Vol. 14 (2001)."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN52387.2021.9534266"},{"key":"e_1_3_2_2_28_1","volume-title":"Proc. of the 36th Conference on Uncertainty in Artificial Intelligence (UAI). PMLR, 809--818","author":"Pawelczyk Martin","year":"2020","unstructured":"Martin Pawelczyk, Klaus Broelemann, and Gjergji. Kasneci. 2020. On Counterfactual Explanations under Predictive Multiplicity. In Proc. of the 36th Conference on Uncertainty in Artificial Intelligence (UAI). PMLR, 809--818."},{"key":"e_1_3_2_2_29_1","volume-title":"Proc. of the Eleventh International Conference on Learning Representations.","author":"Pawelczyk Martin","year":"2023","unstructured":"Martin Pawelczyk, Tobias Leemann, Asia Biega, and Gjergji Kasneci. 2023. On the Trade-Off between Actionable Explanations and the Right to be Forgotten. In Proc. of the Eleventh International Conference on Learning Representations."},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3539597.3573026"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i19.30149"},{"key":"e_1_3_2_2_32_1","first-page":"7","article-title":"A Survey on Graph Counterfactual Explanations: Definitions","volume":"56","author":"Prado-Romero Mario Alfonso","year":"2024","unstructured":"Mario Alfonso Prado-Romero, Bardh Prenkaj, Giovanni Stilo, and Fosca Giannotti. 2024. A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research Challenges. ACM Comput. Surv., Vol. 56, 7 (apr 2024).","journal-title":"Methods, Evaluation, and Research Challenges. ACM Comput. Surv."},{"key":"e_1_3_2_2_33_1","unstructured":"Bardh Prenkaj Mario Villaizan-Vallelado Tobias Leemann and Gjergji Kasneci. 2023. Adapting to Change: Robust Counterfactual Explanations in Dynamic Data Landscapes. arxiv: 2308.02353 [cs.LG]"},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"crossref","unstructured":"Christian Raymond and Giuseppe Riccardi. 2007. Generative and discriminative algorithms for spoken language understanding. In Interspeech 2007--8th Annual Conference of the International Speech Communication Association.","DOI":"10.21437\/Interspeech.2007-448"},{"key":"e_1_3_2_2_35_1","volume-title":"Bioinformatics","volume":"39","author":"R\u00e9au Manon","year":"2023","unstructured":"Manon R\u00e9au, Nicolas Renaud, Li C Xue, and Alexandre MJJ Bonvin. 2023. DeepRank-GNN: a graph neural network framework to learn patterns in protein--protein interfaces. Bioinformatics, Vol. 39, 1 (2023), btac759."},{"key":"e_1_3_2_2_36_1","volume-title":"Learning on Graphs Conference. PMLR, 25--1.","author":"Rossi Emanuele","year":"2024","unstructured":"Emanuele Rossi, Bertrand Charpentier, Francesco Di Giovanni, Fabrizio Frasca, Stephan G\u00fcnnemann, and Michael M Bronstein. 2024. Edge directionality improves learning on heterophilic graphs. In Learning on Graphs Conference. PMLR, 25--1."},{"key":"e_1_3_2_2_37_1","volume-title":"Value of e-commerce losses to online payment fraud worldwide from 2020 to","year":"2023","unstructured":"Statista. 2023. Value of e-commerce losses to online payment fraud worldwide from 2020 to 2023. https:\/\/www.statista.com\/statistics\/1273177\/ecommerce-payment-fraud-losses-globally\/."},{"key":"e_1_3_2_2_38_1","volume-title":"Toward Category-Level Object Recognition","author":"Ulusoy Ilkay","unstructured":"Ilkay Ulusoy and Christopher M Bishop. 2006. Comparison of generative and discriminative techniques for object detection and classification. In Toward Category-Level Object Recognition. Springer, 173--195."},{"key":"e_1_3_2_2_39_1","volume-title":"Advances in Neural Information Processing Systems (NeurIPS)","volume":"34","author":"Upadhyay Sohini","year":"2021","unstructured":"Sohini Upadhyay, Shalmali Joshi, and Himabindu Lakkaraju. 2021. Towards Robust and Reliable Algorithmic Recourse. In Advances in Neural Information Processing Systems (NeurIPS), Vol. 34."},{"key":"e_1_3_2_2_40_1","first-page":"841","article-title":"Counterfactual explanations without opening the black box: Automated decisions and the GDPR","volume":"31","author":"Wachter Sandra","year":"2017","unstructured":"Sandra Wachter, Brent Mittelstadt, and Chris Russell. 2017. Counterfactual explanations without opening the black box: Automated decisions and the GDPR. Harv. JL & Tech., Vol. 31 (2017), 841.","journal-title":"Harv. JL & Tech."},{"key":"e_1_3_2_2_41_1","volume-title":"Dual subgraph-based graph neural network for friendship prediction in location-based social networks. ACM Transactions on Knowledge Discovery from Data (TKDD)","author":"Wei Xuemei","year":"2022","unstructured":"Xuemei Wei, Yezheng Liu, Jianshan Sun, Yuanchun Jiang, Qifeng Tang, and Kun Yuan. 2022. Dual subgraph-based graph neural network for friendship prediction in location-based social networks. ACM Transactions on Knowledge Discovery from Data (TKDD) (2022)."},{"key":"e_1_3_2_2_42_1","volume-title":"Gnnexplainer: Generating explanations for graph neural networks. Advances in neural information processing systems","author":"Ying Z.","year":"2019","unstructured":"Z. Ying, D. Bourgeois, J. You, M. Zitnik, and J. Leskovec. 2019. Gnnexplainer: Generating explanations for graph neural networks. Advances in neural information processing systems, Vol. 32 (2019)."}],"event":{"name":"KDD '24: The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Barcelona Spain","acronym":"KDD '24","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 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671831","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3637528.3671831","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:04:14Z","timestamp":1750291454000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671831"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,24]]},"references-count":42,"alternative-id":["10.1145\/3637528.3671831","10.1145\/3637528"],"URL":"https:\/\/doi.org\/10.1145\/3637528.3671831","relation":{},"subject":[],"published":{"date-parts":[[2024,8,24]]},"assertion":[{"value":"2024-08-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}