{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T03:51:38Z","timestamp":1773892298871,"version":"3.50.1"},"publisher-location":"Cham","reference-count":45,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031746321","type":"print"},{"value":"9783031746338","type":"electronic"}],"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_15","type":"book-chapter","created":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T23:21:12Z","timestamp":1735687272000},"page":"233-249","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["From Black Box to\u00a0Glass Box: Evaluating the\u00a0Faithfulness of\u00a0Process Predictions with\u00a0GCNNs"],"prefix":"10.1007","author":[{"given":"Myriam","family":"Schaschek","sequence":"first","affiliation":[]},{"given":"Fabian","family":"Gwinner","sequence":"additional","affiliation":[]},{"given":"Benedikt","family":"Hein","sequence":"additional","affiliation":[]},{"given":"Axel","family":"Winkelmann","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,1]]},"reference":[{"key":"15_CR1","unstructured":"Agarwal, C., Zitnik, M., Lakkaraju, H.: Probing gnn explainers: a rigorous theoretical and empirical analysis of gnn explanation methods, p.\u00a011. arXiv preprint arXiv:2106.09078v2 (2022)"},{"key":"15_CR2","unstructured":"Berti, A., Van\u00a0Zelst, S.J., van\u00a0der Aalst, W.: Process mining for python (pm4py): bridging the gap between process-and data science, pp.\u00a01\u20134. arXiv preprint arXiv:1905.06169 (2019)"},{"key":"15_CR3","unstructured":"Doshi-Velez, F., Kim, B.: Towards a rigorous science of interpretable machine learning, pp. 1\u201313. arXiv preprint arXiv:1702.08608. (2017)"},{"issue":"1","key":"15_CR4","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1145\/3359786","volume":"63","author":"M Du","year":"2019","unstructured":"Du, M., Liu, N., Hu, X.: Techniques for interpretable machine learning. Commun. ACM 63(1), 68\u201377 (2019)","journal-title":"Commun. ACM"},{"key":"15_CR5","doi-asserted-by":"crossref","unstructured":"Faber, L., K.\u00a0Moghaddam, A., Wattenhofer, R.: When comparing to ground truth is wrong: on evaluating gnn explanation methods. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 332\u2013341 (2021)","DOI":"10.1145\/3447548.3467283"},{"key":"15_CR6","unstructured":"Fey, M., Lenssen, J.E.: Fast graph representation learning with pytorch geometric, pp.\u00a01\u20139. arXiv preprint arXiv:1903.02428 (2019)"},{"key":"15_CR7","unstructured":"Hagberg, A., Swart, P., S\u00a0Chult, D.: Exploring network structure, dynamics, and function using networkx. Tech. rep., Los Alamos National Lab.(LANL), Los Alamos, NM (United States) (2008)"},{"issue":"sup1","key":"15_CR8","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1080\/12460125.2020.1780780","volume":"29","author":"M Harl","year":"2020","unstructured":"Harl, M., Weinzierl, S., Stierle, M., Matzner, M.: Explainable predictive business process monitoring using gated graph neural networks. J. Decis. Syst. 29(sup1), 312\u2013327 (2020)","journal-title":"J. Decis. Syst."},{"key":"15_CR9","unstructured":"Hein, B., Schaschek, M.: GitHub - myrmsch\/From-Black-Box-to-Glass-Box-Evaluating-Faithfulness-of-Process-Predictions-with-GCNNs \u2014 github.com. https:\/\/github.com\/myrmsch\/From-Black-Box-to-Glass-Box-Evaluating-Faithfulness-of-Process-Predictions-with-GCNNs\/tree\/main, (Accessed 31-07-2023)"},{"key":"15_CR10","unstructured":"Hooker, S., Erhan, D., Kindermans, P.J., Kim, B.: A benchmark for interpretability methods in deep neural networks. Adv. Neural Inform. Process. Syst. 32 (2019)"},{"key":"15_CR11","doi-asserted-by":"crossref","unstructured":"Jacovi, A., Goldberg, Y.: Towards faithfully interpretable nlp systems: How should we define and evaluate faithfulness?, pp. 1\u201315. arXiv preprint arXiv:2004.03685 (2020)","DOI":"10.18653\/v1\/2020.acl-main.386"},{"key":"15_CR12","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization, pp. 1\u201315. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"15_CR13","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks, pp. 1\u201314 (2016)"},{"key":"15_CR14","unstructured":"Li, M.M., Huang, K., Zitnik, M.: Representation learning for networks in biology and medicine: Advancements, challenges, and opportunities pp. 1\u201318. arXiv e-prints: arXiv: 2104.04883 (2021)"},{"key":"15_CR15","doi-asserted-by":"crossref","unstructured":"Li, P., Yang, Y., Pagnucco, M., Song, Y.: Explainability in graph neural networks: an experimental survey, pp.\u00a01\u20138 . arXiv preprint arXiv:2203.09258. (2022)","DOI":"10.1109\/IJCNN55064.2022.9892241"},{"issue":"240","key":"15_CR16","first-page":"1","volume":"22","author":"M Liu","year":"2021","unstructured":"Liu, M., et al.: Dig: a turnkey library for diving into graph deep learning research. J. Mach. Learn. Res. 22(240), 1\u20139 (2021)","journal-title":"J. Mach. Learn. Res."},{"key":"15_CR17","first-page":"19620","volume":"33","author":"D Luo","year":"2020","unstructured":"Luo, D., et al.: Parameterized explainer for graph neural network. Adv. Neural. Inf. Process. Syst. 33, 19620\u201319631 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"15_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.artint.2018.07.007","volume":"267","author":"T Miller","year":"2019","unstructured":"Miller, T.: Explanation in artificial intelligence: insights from the social sciences. Artif. Intell. 267, 1\u201338 (2019)","journal-title":"Artif. Intell."},{"key":"15_CR19","unstructured":"Oberdorf, F., Schaschek, M., Stein, N., Flath, C.M.: Neural process mining: multi-headed predictive process analytics in practice. In: Proceedings of the 29th European Conference on Information Systems (ECIS) (2021)"},{"key":"15_CR20","unstructured":"Paszke, A., et\u00a0al.: Pytorch: An imperative style, high-performance deep learning library. Adv. Neural Inform. Process. Syst. 32 (2019)"},{"key":"15_CR21","doi-asserted-by":"crossref","unstructured":"Philipp, P., Georgi, R.X.M., Beyerer, J., Robert, S.: Analysis of control flow graphs using graph convolutional neural networks. In: 2019 6th International Conference on Soft Computing & Machine Intelligence (ISCMI), pp. 73\u201377. IEEE (2019)","DOI":"10.1109\/ISCMI47871.2019.9004296"},{"key":"15_CR22","doi-asserted-by":"crossref","unstructured":"Pope, P.E., Kolouri, S., Rostami, M., Martin, C.E., Hoffmann, H.: Explainability methods for graph convolutional neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10772\u201310781 (2019)","DOI":"10.1109\/CVPR.2019.01103"},{"key":"15_CR23","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: Why should i trust you? explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135\u20131144 (2016)","DOI":"10.1145\/2939672.2939778"},{"key":"15_CR24","unstructured":"Rizzi, W., et al.: Explainable predictive process monitoring: A user evaluation, p.\u00a051. arXiv preprint arXiv:2202.07760 (2022)"},{"issue":"5","key":"15_CR25","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","volume":"1","author":"C Rudin","year":"2019","unstructured":"Rudin, C.: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 1(5), 206\u2013215 (2019)","journal-title":"Nat. Mach. Intell."},{"key":"15_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.113100","volume":"144","author":"S Sachan","year":"2020","unstructured":"Sachan, S., Yang, J.B., Xu, D.L., Benavides, D.E., Li, Y.: An explainable ai decision-support-system to automate loan underwriting. Expert Syst. Appl. 144, 113100 (2020)","journal-title":"Expert Syst. Appl."},{"key":"15_CR27","first-page":"5898","volume":"33","author":"B Sanchez-Lengeling","year":"2020","unstructured":"Sanchez-Lengeling, B., et al.: Evaluating attribution for graph neural networks. Adv. Neural. Inf. Process. Syst. 33, 5898\u20135910 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"15_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijhcs.2020.102551","volume":"146","author":"D Shin","year":"2021","unstructured":"Shin, D.: The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable ai. Int. J. Hum Comput Stud. 146, 102551 (2021)","journal-title":"Int. J. Hum Comput Stud."},{"key":"15_CR29","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1007\/978-3-030-65310-1_31","volume-title":"Service-Oriented Computing","author":"R Sindhgatta","year":"2020","unstructured":"Sindhgatta, R., Ouyang, C., Moreira, C.: Exploring interpretability for predictive process analytics. In: Kafeza, E., Benatallah, B., Martinelli, F., Hacid, H., Bouguettaya, A., Motahari, H. (eds.) ICSOC 2020. LNCS, vol. 12571, pp. 439\u2013447. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-65310-1_31"},{"key":"15_CR30","unstructured":"Stierle, M., Brunk, J., Weinzierl, S., Zilker, S., Matzner, M., Becker, J.: Bringing light into the darkness-a systematic literature review on explainable predictive business process monitoring techniques. In: ECIS 2021 Research-in-Progress Papers, p.\u00a08 (2021)"},{"key":"15_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2021.113511","volume":"144","author":"M Stierle","year":"2021","unstructured":"Stierle, M., Weinzierl, S., Harl, M., Matzner, M.: A technique for determining relevance scores of process activities using graph-based neural networks. Decis. Support Syst. 144, 113511 (2021)","journal-title":"Decis. Support Syst."},{"key":"15_CR32","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1007\/978-3-030-58666-9_14","volume-title":"Business Process Management","author":"F Taymouri","year":"2020","unstructured":"Taymouri, F., Rosa, M.L., Erfani, S., Bozorgi, Z.D., Verenich, I.: Predictive business process monitoring via generative adversarial nets: the case of next event prediction. In: Fahland, D., Ghidini, C., Becker, J., Dumas, M. (eds.) BPM 2020. LNCS, vol. 12168, pp. 237\u2013256. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58666-9_14"},{"issue":"2","key":"15_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3301300","volume":"13","author":"I Teinemaa","year":"2019","unstructured":"Teinemaa, I., Dumas, M., Rosa, M.L., Maggi, F.M.: Outcome-oriented predictive process monitoring: review and benchmark. ACM Trans. Knowl. Dis. Data (TKDD) 13(2), 1\u201357 (2019)","journal-title":"ACM Trans. Knowl. Dis. Data (TKDD)"},{"key":"15_CR34","doi-asserted-by":"crossref","unstructured":"Velmurugan, M., Ouyang, C., Moreira, C., Sindhgatta, R.: Evaluating explainable methods for predictive process analytics: A functionally-grounded approach, p.\u00a015. arXiv preprint arXiv:2012.04218 (2020)","DOI":"10.1007\/978-3-030-79108-7_8"},{"key":"15_CR35","unstructured":"Velmurugan, M., Ouyang, C., Moreira, C., Sindhgatta, R.: Developing a fidelity evaluation approach for interpretable machine learning pp. 1\u201328. arXiv preprint arXiv:2106.08492 (2021)"},{"key":"15_CR36","series-title":"Lecture Notes in Business Information Processing","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1007\/978-3-030-79108-7_8","volume-title":"Intelligent Information Systems","author":"M Velmurugan","year":"2021","unstructured":"Velmurugan, M., Ouyang, C., Moreira, C., Sindhgatta, R.: Evaluating fidelity of explainable methods for predictive process analytics. In: Nurcan, S., Korthaus, A. (eds.) CAiSE 2021. LNBIP, vol. 424, pp. 64\u201372. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-79108-7_8"},{"key":"15_CR37","doi-asserted-by":"crossref","unstructured":"Venugopal, I., T\u00f6llich, J., Fairbank, M., Scherp, A.: A comparison of deep-learning methods for analysing and predicting business processes. In: 2021 International Joint Conference on Neural Networks (IJCNN), pp.\u00a01\u20138. IEEE (2021)","DOI":"10.1109\/IJCNN52387.2021.9533742"},{"key":"15_CR38","unstructured":"Wanner, J., Heinrich, K., Janiesch, C., Zschech, P.: How much ai do you require? decision factors for adopting ai technology. In: Proceedings of the 31st International Conference on Information Systems (ICIS), p.\u00a010 (2020)"},{"key":"15_CR39","series-title":"Lecture Notes in Business Information Processing","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1007\/978-3-030-94343-1_3","volume-title":"Business Process Management Workshops","author":"S Weinzierl","year":"2022","unstructured":"Weinzierl, S.: Exploring gated graph sequence neural networks for predicting next process activities. In: Marrella, A., Weber, B. (eds.) BPM 2021. LNBIP, vol. 436, pp. 30\u201342. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-030-94343-1_3"},{"key":"15_CR40","series-title":"Lecture Notes in Business Information Processing","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1007\/978-3-030-66498-5_10","volume-title":"Business Process Management Workshops","author":"S Weinzierl","year":"2020","unstructured":"Weinzierl, S., Zilker, S., Brunk, J., Revoredo, K., Matzner, M., Becker, J.: XNAP: Making LSTM-based next activity predictions explainable by using LRP. In: Del R\u00edo Ortega, A., Leopold, H., Santoro, F.M. (eds.) BPM 2020. LNBIP, vol. 397, pp. 129\u2013141. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-66498-5_10"},{"key":"15_CR41","doi-asserted-by":"crossref","unstructured":"Wickramanayake, B., He, Z., Ouyang, C., Moreira, C., Xu, Y., Sindhgatta, R.: Building interpretable models for business process prediction using shared and specialised attention mechanisms, p.\u00a040. arXiv preprint arXiv:2109.01419 (2021)","DOI":"10.1016\/j.knosys.2022.108773"},{"key":"15_CR42","doi-asserted-by":"crossref","unstructured":"Wiegreffe, S., Pinter, Y.: Attention is not not explanation. arXiv preprint arXiv:1908.04626 (2019)","DOI":"10.18653\/v1\/D19-1002"},{"issue":"1","key":"15_CR43","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","volume":"32","author":"Z Wu","year":"2021","unstructured":"Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE Trans. Neural Netw. Learn. Syst. 32(1), 4\u201324 (2021)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"15_CR44","unstructured":"Ying, Z., Bourgeois, D., You, J., Zitnik, M., Leskovec, J.: Gnnexplainer: generating explanations for graph neural networks. Adv. Neural Inform. Process. Syst. 32 (2019)"},{"key":"15_CR45","unstructured":"Yuan, H., Yu, H., Gui, S., Ji, S.: Explainability in graph neural networks: a taxonomic survey, p.\u00a014. arXiv preprint arXiv:2012.15445 (2020)"}],"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_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,8]],"date-time":"2025-03-08T04:40:18Z","timestamp":1741408818000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-74633-8_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031746321","9783031746338"],"references-count":45,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-74633-8_15","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"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"}}]}}