{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T08:49:39Z","timestamp":1760345379048,"version":"3.40.3"},"publisher-location":"Cham","reference-count":48,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031746321"},{"type":"electronic","value":"9783031746338"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-74633-8_10","type":"book-chapter","created":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T23:21:51Z","timestamp":1735687311000},"page":"152-170","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Are Generative-Based Graph Counterfactual Explainers Worth It?"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0491-3515","authenticated-orcid":false,"given":"Mario Alfonso","family":"Prado-Romero","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2991-2279","authenticated-orcid":false,"given":"Bardh","family":"Prenkaj","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2092-0213","authenticated-orcid":false,"given":"Giovanni","family":"Stilo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,1]]},"reference":[{"key":"10_CR1","doi-asserted-by":"crossref","unstructured":"Abrate, C., Bonchi, F.: Counterfactual graphs for explainable classification of brain networks. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 2495\u20132504 (2021)","DOI":"10.1145\/3447548.3467154"},{"key":"10_CR2","doi-asserted-by":"crossref","unstructured":"Aragona, D., Podo, L., Prenkaj, B., Velardi, P.: Coronna: a deep sequential framework to predict epidemic spread. In: Proceedings of the 36th Annual ACM Symposium on Applied Computing, pp. 10\u201317 (2021)","DOI":"10.1145\/3412841.3441883"},{"key":"10_CR3","first-page":"5644","volume":"34","author":"M Bajaj","year":"2021","unstructured":"Bajaj, M., et al.: Robust counterfactual explanations on graph neural networks. Adv. Neural. Inf. Process. Syst. 34, 5644\u20135655 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"9","key":"10_CR4","doi-asserted-by":"publisher","first-page":"1616","DOI":"10.1109\/TKDE.2018.2807452","volume":"30","author":"H Cai","year":"2018","unstructured":"Cai, H., Zheng, V.W., Chang, K.C.C.: A comprehensive survey of graph embedding: problems, techniques, and applications. IEEE Trans. Knowl. Data Eng. 30(9), 1616\u20131637 (2018)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10_CR5","unstructured":"Dabkowski, P., Gal, Y.: Real time image saliency for black box classifiers. Advances in neural information processing systems 30 (2017)"},{"key":"10_CR6","doi-asserted-by":"crossref","unstructured":"Ding, M., Yang, K., Yeung, D.Y., Pong, T.C.: Effective feature learning with unsupervised learning for improving the predictive models in massive open online courses. In: Proceedings of the 9th International Conference on Learning Analytics & Knowledge, pp. 135\u2013144 (2019)","DOI":"10.1145\/3303772.3303795"},{"key":"10_CR7","unstructured":"Faber, L., Moghaddam, A.K., Wattenhofer, R.: Contrastive graph neural network explanation. In: Proc. of the 37th Graph Repr. Learning and Beyond Workshop at ICML 2020, p.\u00a028. Int. Conf. on Machine Learning (2020)"},{"key":"10_CR8","doi-asserted-by":"crossref","unstructured":"Feng, W., Tang, J., Liu, T.X.: Understanding dropouts in moocs. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a033, pp. 517\u2013524 (2019)","DOI":"10.1609\/aaai.v33i01.3301517"},{"key":"10_CR9","doi-asserted-by":"crossref","unstructured":"Guidotti, R.: Counterfactual explanations and how to find them: literature review and benchmarking. Data Mining and Knowledge Discovery, pp. 1\u201355 (2022)","DOI":"10.1007\/s10618-022-00831-6"},{"issue":"5","key":"10_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3236009","volume":"51","author":"R Guidotti","year":"2018","unstructured":"Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., Pedreschi, D.: A survey of methods for explaining black box models. ACM Comput. Surv. (CSUR) 51(5), 1\u201342 (2018)","journal-title":"ACM Comput. Surv. (CSUR)"},{"issue":"1","key":"10_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-77766-9","volume":"10","author":"K Huang","year":"2020","unstructured":"Huang, K., Xiao, C., Glass, L.M., Zitnik, M., Sun, J.: Skipgnn: predicting molecular interactions with skip-graph networks. Sci. Rep. 10(1), 1\u201316 (2020)","journal-title":"Sci. Rep."},{"key":"10_CR12","doi-asserted-by":"crossref","unstructured":"Huang, Z., Kosan, M., Medya, S., Ranu, S., Singh, A.: Global counterfactual explainer for graph neural networks. In: Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, pp. 141\u2013149 (2023)","DOI":"10.1145\/3539597.3570376"},{"key":"10_CR13","doi-asserted-by":"crossref","unstructured":"Kolesnikov, A., et al.: Big transfer (bit): general visual representation learning. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part V 16, pp. 491\u2013507. Springer (2020)","DOI":"10.1007\/978-3-030-58558-7_29"},{"key":"10_CR14","doi-asserted-by":"crossref","unstructured":"Liu, Y., Chen, C., Liu, Y., Zhang, X., Xie, S.: Multi-objective explanations of gnn predictions. In: 2021 IEEE International Conference on Data Mining (ICDM), pp. 409\u2013418. IEEE (2021)","DOI":"10.1109\/ICDM51629.2021.00052"},{"key":"10_CR15","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1007\/s10044-012-0284-8","volume":"16","author":"L Livi","year":"2013","unstructured":"Livi, L., Rizzi, A.: The graph matching problem. Pattern Anal. Appl. 16, 253\u2013283 (2013)","journal-title":"Pattern Anal. Appl."},{"key":"10_CR16","doi-asserted-by":"crossref","unstructured":"Loyola-Gonz\u00e1lez, O.: Black-box vs. white-box: Understanding their advantages and weaknesses from a practical point of view. IEEE Access 7, 154096\u2013154113 (2019)","DOI":"10.1109\/ACCESS.2019.2949286"},{"key":"10_CR17","unstructured":"Ma, J., Guo, R., Mishra, S., Zhang, A., Li, J.: CLEAR: generative counterfactual explanations on graphs. In: NeurIPS (2022). http:\/\/papers.nips.cc\/paper_files\/paper\/2022\/hash\/a69d7f3a1340d55c720e572742439eaf-Abstract-Conference.html"},{"issue":"1","key":"10_CR18","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1504\/IJDMB.2020.109502","volume":"24","author":"L Madeddu","year":"2020","unstructured":"Madeddu, L., Stilo, G., Velardi, P.: A feature-learning-based method for the disease-gene prediction problem. Int. J. Data Min. Bioinform. 24(1), 16\u201337 (2020)","journal-title":"Int. J. Data Min. Bioinform."},{"key":"10_CR19","unstructured":"Nemirovsky, D., Thiebaut, N., Xu, Y., Gupta, A.: Countergan: Generating counterfactuals for real-time recourse and interpretability using residual gans. In: Cussens, J., Zhang, K. (eds.) Uncertainty in Artificial Intelligence, Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence, UAI 2022, 1-5 August 2022, Eindhoven, The Netherlands. Proceedings of Machine Learning Research, vol.\u00a0180, pp. 1488\u20131497. PMLR (2022), https:\/\/proceedings.mlr.press\/v180\/nemirovsky22a.html"},{"key":"10_CR20","doi-asserted-by":"crossref","unstructured":"Nguyen, T.M., Quinn, T.P., Nguyen, T., Tran, T.: Explaining black box drug target prediction through model agnostic counterfactual samples. IEEE\/ACM Trans. Comput. Biol. Bioinform. (2022)","DOI":"10.1109\/TCBB.2022.3190266"},{"key":"10_CR21","doi-asserted-by":"crossref","unstructured":"Numeroso, D., Bacciu, D.: Meg: Generating molecular counterfactual explanations for deep graph networks. In: 2021 International Joint Conference on Neural Networks (IJCNN), pp.\u00a01\u20138. IEEE (2021)","DOI":"10.1109\/IJCNN52387.2021.9534266"},{"key":"10_CR22","doi-asserted-by":"crossref","unstructured":"Petch, J., Di, S., Nelson, W.: Opening the black box: the promise and limitations of explainable machine learning in cardiology. Canadian J. Cardiol. (2021)","DOI":"10.1016\/j.cjca.2021.09.004"},{"key":"10_CR23","doi-asserted-by":"crossref","unstructured":"Prado-Romero, M.A., Prenkaj, B., Stilo, G.: Developing and evaluating graph counterfactual explanation with gretel. In: Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, pp. 1180\u20131183 (2023)","DOI":"10.1145\/3539597.3573026"},{"key":"10_CR24","unstructured":"Prado-Romero, M.A., Prenkaj, B., Stilo, G.: Revisiting countergan for counterfactual explainability of graphs. In: Maughan, K., Liu, R., Burns, T.F. (eds.) The First Tiny Papers Track at ICLR 2023, Tiny Papers @ ICLR 2023, Kigali, Rwanda, May 5, 2023. OpenReview.net (2023). https:\/\/openreview.net\/pdf?id=d0m0Rl15q3g"},{"key":"10_CR25","doi-asserted-by":"publisher","unstructured":"Prado-Romero, M.A., Prenkaj, B., Stilo, G., Giannotti, F.: A survey on graph counterfactual explanations: Definitions, methods, evaluation, and research challenges. ACM Comput. Surv. (2023). https:\/\/doi.org\/10.1145\/3618105. https:\/\/doi.org\/10.1145\/3618105","DOI":"10.1145\/3618105"},{"key":"10_CR26","doi-asserted-by":"crossref","unstructured":"Prado-Romero, M.A., Stilo, G.: Gretel: graph counterfactual explanation evaluation framework. In: Proceedings of the 31st ACM International Conference on Information & Knowledge Management, pp. 4389\u20134393 (2022)","DOI":"10.1145\/3511808.3557608"},{"key":"10_CR27","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1016\/j.future.2021.07.002","volume":"125","author":"B Prenkaj","year":"2021","unstructured":"Prenkaj, B., Distante, D., Faralli, S., Velardi, P.: Hidden space deep sequential risk prediction on student trajectories. Futur. Gener. Comput. Syst. 125, 532\u2013543 (2021)","journal-title":"Futur. Gener. Comput. Syst."},{"key":"10_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2022.102454","volume":"135","author":"B Prenkaj","year":"2023","unstructured":"Prenkaj, B., et al.: A self-supervised algorithm to detect signs of social isolation in the elderly from daily activity sequences. Artif. Intell. Med. 135, 102454 (2023)","journal-title":"Artif. Intell. Med."},{"key":"10_CR29","doi-asserted-by":"crossref","unstructured":"Prenkaj, B., Velardi, P., Distante, D., Faralli, S.: A reproducibility study of deep and surface machine learning methods for human-related trajectory prediction. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 2169\u20132172 (2020)","DOI":"10.1145\/3340531.3412088"},{"issue":"1","key":"10_CR30","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","volume":"20","author":"F Scarselli","year":"2008","unstructured":"Scarselli, F., Gori, M., Tsoi, A., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Networks 20(1), 61\u201380 (2008)","journal-title":"IEEE Trans. Neural Networks"},{"key":"10_CR31","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618\u2013626 (2017)","DOI":"10.1109\/ICCV.2017.74"},{"key":"10_CR32","unstructured":"Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: Visualising image classification models and saliency maps. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Workshop Track Proceedings (2014). http:\/\/arxiv.org\/abs\/1312.6034"},{"key":"10_CR33","doi-asserted-by":"crossref","unstructured":"Tan, J., et al.: Learning and evaluating graph neural network explanations based on counterfactual and factual reasoning. In: Proceedings of the ACM Web Conference 2022, WWW 2022, pp. 1018\u20131027. Association for Computing Machinery, New York (2022). https:\/\/doi.org\/10.1145\/3485447.3511948","DOI":"10.1145\/3485447.3511948"},{"issue":"6","key":"10_CR34","doi-asserted-by":"publisher","DOI":"10.1002\/smr.2170","volume":"31","author":"I Verenich","year":"2019","unstructured":"Verenich, I., Dumas, M., La Rosa, M., Nguyen, H.: Predicting process performance: a white-box approach based on process models. J. Softw. Evol. Process 31(6), e2170 (2019)","journal-title":"J. Softw. Evol. Process"},{"key":"10_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.116611","volume":"195","author":"H Verma","year":"2022","unstructured":"Verma, H., Mandal, S., Gupta, A.: Temporal deep learning architecture for prediction of covid-19 cases in india. Expert Syst. Appl. 195, 116611 (2022)","journal-title":"Expert Syst. Appl."},{"issue":"2","key":"10_CR36","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1007\/s10044-021-01055-y","volume":"25","author":"T Vermeire","year":"2022","unstructured":"Vermeire, T., Brughmans, D., Goethals, S., de Oliveira, R.M.B., Martens, D.: Explainable image classification with evidence counterfactual. Pattern Anal. Appl. 25(2), 315\u2013335 (2022)","journal-title":"Pattern Anal. Appl."},{"key":"10_CR37","doi-asserted-by":"crossref","unstructured":"Wang, W., Yu, H., Miao, C.: Deep model for dropout prediction in moocs. In: Proceedings of the 2nd International Conference on Crowd Science and Engineering, pp. 26\u201332 (2017)","DOI":"10.1145\/3126973.3126990"},{"key":"10_CR38","doi-asserted-by":"crossref","unstructured":"Wei, X., Liu, Y., Sun, J., Jiang, Y., Tang, Q., Yuan, K.: Dual subgraph-based graph neural network for friendship prediction in location-based social networks. ACM Trans. Knowl. Discovery Data (TKDD) (2022)","DOI":"10.1145\/3554981"},{"issue":"13","key":"10_CR39","doi-asserted-by":"publisher","first-page":"3697","DOI":"10.1039\/D1SC05259D","volume":"13","author":"GP Wellawatte","year":"2022","unstructured":"Wellawatte, G.P., Seshadri, A., White, A.D.: Model agnostic generation of counterfactual explanations for molecules. Chem. Sci. 13(13), 3697\u20133705 (2022)","journal-title":"Chem. Sci."},{"key":"10_CR40","doi-asserted-by":"crossref","unstructured":"Wu, S., Tang, Y., Zhu, Y., Wang, L., Xie, X., Tan, T.: Session-based recommendation with graph neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a033, pp. 346\u2013353 (2019)","DOI":"10.1609\/aaai.v33i01.3301346"},{"key":"10_CR41","doi-asserted-by":"crossref","unstructured":"Wu, X., et al.: Clare: a semi-supervised community detection algorithm. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 2059\u20132069 (2022)","DOI":"10.1145\/3534678.3539370"},{"key":"10_CR42","doi-asserted-by":"publisher","unstructured":"Xu, L., Xi, W., Wang, C.: Session-based recommendation with heterogeneous graph neural networks. In: International Joint Conference on Neural Networks, IJCNN 2021, Shenzhen, China, July 18-22, 2021, pp.\u00a01\u20138. IEEE (2021). https:\/\/doi.org\/10.1109\/IJCNN52387.2021.9533519, https:\/\/doi.org\/10.1109\/IJCNN52387.2021.9533519","DOI":"10.1109\/IJCNN52387.2021.9533519"},{"key":"10_CR43","doi-asserted-by":"crossref","unstructured":"Xu, Z., Lamba, H., Ai, Q., Tetreault, J., Jaimes, A.: Counterfactual editing for search result explanation. arXiv preprint arXiv:2301.10389 (2023)","DOI":"10.1145\/3664190.3672508"},{"key":"10_CR44","doi-asserted-by":"publisher","unstructured":"Yang, L., Kenny, E., Ng, T.L.J., Yang, Y., Smyth, B., Dong, R.: Generating plausible counterfactual explanations for deep transformers in financial text classification. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 6150\u20136160. International Committee on Computational Linguistics, Barcelona, Spain (Online), December 2020. https:\/\/doi.org\/10.18653\/v1\/2020.coling-main.541, https:\/\/aclanthology.org\/2020.coling-main.541","DOI":"10.18653\/v1\/2020.coling-main.541"},{"key":"10_CR45","unstructured":"Ying, Z., Bourgeois, D., You, J., Zitnik, M., Leskovec, J.: Gnnexplainer: Generating explanations for graph neural networks. In: Wallach, H.M., Larochelle, H., Beygelzimer, A., d\u2019Alch\u00e9-Buc, F., Fox, E.B., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada, pp. 9240\u20139251 (2019). https:\/\/proceedings.neurips.cc\/paper\/2019\/hash\/d80b7040b773199015de6d3b4293c8ff-Abstract.html"},{"key":"10_CR46","doi-asserted-by":"publisher","unstructured":"Yuan, H., Yu, H., Gui, S., Ji, S.: Explainability in graph neural networks: A taxonomic survey. IEEE Trans. Pattern Anal. Mach. Intell. 45(5), 5782\u20135799 (2023). https:\/\/doi.org\/10.1109\/TPAMI.2022.3204236","DOI":"10.1109\/TPAMI.2022.3204236"},{"key":"10_CR47","doi-asserted-by":"crossref","unstructured":"Zemni, M., Chen, M., Zablocki, E., Ben-Younes, H., P\u00e9rez, P., Cord, M.: Octet: Object-aware counterfactual explanations. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 15062\u201315071 (June 2023)","DOI":"10.1109\/CVPR52729.2023.01446"},{"key":"10_CR48","doi-asserted-by":"crossref","unstructured":"Zhang, L., Long, C., Zhang, X., Xiao, C.: Ris-gan: explore residual and illumination with generative adversarial networks for shadow removal. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a034, pp. 12829\u201312836 (2020)","DOI":"10.1609\/aaai.v34i07.6979"}],"container-title":["Communications in Computer and Information Science","Machine Learning and Principles and Practice of Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-74633-8_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,8]],"date-time":"2025-03-08T04:40:32Z","timestamp":1741408832000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-74633-8_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031746321","9783031746338"],"references-count":48,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-74633-8_10","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"1 January 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Turin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2023.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}