{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T09:00:55Z","timestamp":1784970055404,"version":"3.55.0"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032006554","type":"print"},{"value":"9783032006561","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,8,20]],"date-time":"2025-08-20T00:00:00Z","timestamp":1755648000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,20]],"date-time":"2025-08-20T00:00:00Z","timestamp":1755648000000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-00656-1_3","type":"book-chapter","created":{"date-parts":[[2025,8,19]],"date-time":"2025-08-19T09:03:45Z","timestamp":1755594225000},"page":"31-45","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["GNN\u2019s Uncertainty Quantification Using Self-distillation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6210-9306","authenticated-orcid":false,"given":"Hirad","family":"Daneshvar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6768-0168","authenticated-orcid":false,"given":"Reza","family":"Samavi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,8,20]]},"reference":[{"issue":"6","key":"3_CR1","doi-asserted-by":"publisher","DOI":"10.1002\/aic.17696","volume":"68","author":"ARN Aouichaoui","year":"2022","unstructured":"Aouichaoui, A.R.N., Mansouri, S.S., Abildskov, J., Sin, G.: Uncertainty estimation in deep learning-based property models: graph neural networks applied to the critical properties. AIChE J. 68(6), e17696 (2022). https:\/\/doi.org\/10.1002\/aic.17696","journal-title":"AIChE J."},{"key":"3_CR2","doi-asserted-by":"crossref","unstructured":"Chen, Y., Bian, Y., Xiao, X., Rong, Y., Xu, T., Huang, J.: On self-distilling graph neural network (2021). https:\/\/arxiv.org\/abs\/2011.02255","DOI":"10.24963\/ijcai.2021\/314"},{"key":"3_CR3","doi-asserted-by":"publisher","unstructured":"Daneshvar, H., et al.: SOK: application of machine learning models in child and youth mental health decision-making. Artif. Intell. Med., 113\u2013132 (2024). https:\/\/doi.org\/10.1016\/B978-0-443-13671-9.00003-X","DOI":"10.1016\/B978-0-443-13671-9.00003-X"},{"key":"3_CR4","doi-asserted-by":"publisher","unstructured":"Daneshvar, H., Samavi, R.: Heterogeneous patient graph embedding in readmission prediction. Proc. Can. Conf. Artif. Intell. (2022). https:\/\/doi.org\/10.21428\/594757db.869abbde","DOI":"10.21428\/594757db.869abbde"},{"key":"3_CR5","unstructured":"Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. J. Mach. Learning Res. 24(43), 1\u201348 (2023). http:\/\/jmlr.org\/papers\/v24\/22-0567.html"},{"key":"3_CR6","doi-asserted-by":"publisher","unstructured":"Gheshlaghi, S.H., Soltani, N.Y., Ganji, M.: Uncertainty estimation for out-of-distribution detection of whole slide images. In: ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.\u00a01\u20135 (2025). https:\/\/doi.org\/10.1109\/ICASSP49660.2025.10889349","DOI":"10.1109\/ICASSP49660.2025.10889349"},{"issue":"6","key":"3_CR7","doi-asserted-by":"publisher","first-page":"1789","DOI":"10.1007\/s11263-021-01453-z","volume":"129","author":"J Gou","year":"2021","unstructured":"Gou, J., Yu, B., Maybank, S.J., Tao, D.: Knowledge distillation: a survey. Int. J. Comput. Vis. 129(6), 1789\u20131819 (2021). https:\/\/doi.org\/10.1007\/s11263-021-01453-z","journal-title":"Int. J. Comput. Vis."},{"key":"3_CR8","unstructured":"H.\u00a0Zargarbashi, S., Antonelli, S., Bojchevski, A.: Conformal prediction sets for graph neural networks. In: Krause, A., Brunskill, E., Cho, K., Engelhardt, B., Sabato, S., Scarlett, J. (eds.) Proceedings of the 40th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol.\u00a0202, pp. 12292\u201312318. PMLR (2023). https:\/\/proceedings.mlr.press\/v202\/h-zargarbashi23a.html"},{"key":"3_CR9","unstructured":"Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol.\u00a030. Curran Associates, Inc. (2017). https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2017\/file\/5dd9db5e033da9c6fb5ba83c7a7ebea9-Paper.pdf"},{"key":"3_CR10","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network (2015). https:\/\/arxiv.org\/abs\/1503.02531"},{"key":"3_CR11","doi-asserted-by":"crossref","unstructured":"Huang, K., Jin, Y., Candes, E., Leskovec, J.: Uncertainty quantification over graph with conformalized graph neural networks. In: Oh, A., Naumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S. (eds.) Advances in Neural Information Processing Systems. vol.\u00a036, pp. 26699\u201326721. Curran Associates, Inc. (2023). https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2023\/file\/54a1495b06c4ee2f07184afb9a37abda-Paper-Conference.pdf","DOI":"10.52202\/075280-1160"},{"key":"3_CR12","unstructured":"Johnson, A., et al.: MIMIC-IV (2024)"},{"key":"3_CR13","doi-asserted-by":"crossref","unstructured":"Karimi, H., Samavi, R.: Quantifying deep learning model uncertainty in conformal prediction. In: Proceedings of the AAAI Symposium Series, vol.\u00a01, pp. 142\u2013148 (2023)","DOI":"10.1609\/aaaiss.v1i1.27492"},{"key":"3_CR14","unstructured":"Karimi, H., Samavi, R.: Evidential uncertainty sets in deep classifiers using conformal prediction. In: Proceedings of the Thirteenth Symposium on Conformal and Probabilistic Prediction with Applications, PMLR, vol.\u00a0230, pp. 466\u2013489 (2024)"},{"issue":"1","key":"3_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-021-00579-z","volume":"14","author":"Y Kwon","year":"2022","unstructured":"Kwon, Y., Lee, D., Choi, Y.-S., Kang, S.: Uncertainty-aware prediction of chemical reaction yields with graph neural networks. J. Cheminformatics 14(1), 1\u201310 (2022). https:\/\/doi.org\/10.1186\/s13321-021-00579-z","journal-title":"J. Cheminformatics"},{"key":"3_CR16","unstructured":"Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol.\u00a030. Curran Associates, Inc. (2017). https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2017\/file\/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf"},{"key":"3_CR17","doi-asserted-by":"crossref","unstructured":"Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 1196\u20131205. IEEE (2020)","DOI":"10.1109\/BigData50022.2020.9377795"},{"key":"3_CR18","unstructured":"Morris, C., Kriege, N.M., Bause, F., Kersting, K., Mutzel, P., Neumann, M.: TUDataset: a collection of benchmark datasets for learning with graphs. In: ICML 2020 Workshop on Graph Representation Learning and Beyond (GRL+ 2020) (2020). www.graphlearning.io"},{"key":"3_CR19","unstructured":"Morris, C., et al.: Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks (2021)"},{"key":"3_CR20","unstructured":"Phuong, M., Lampert, C.: Towards understanding knowledge distillation. In: Chaudhuri, K., Salakhutdinov, R. (eds.) Proceedings of the 36th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol.\u00a097, pp. 5142\u20135151. PMLR (2019). https:\/\/proceedings.mlr.press\/v97\/phuong19a.html"},{"key":"3_CR21","doi-asserted-by":"crossref","unstructured":"Shang, J., Ma, T., Xiao, C., Sun, J.: Pre-training of graph augmented transformers for medication recommendation (2019)","DOI":"10.24963\/ijcai.2019\/825"},{"key":"3_CR22","doi-asserted-by":"publisher","unstructured":"Vovk, V., Gammerman, A., Shafer, G.: Algorithmic learning in a random world, vol.\u00a029. Springer, Cham (2005). https:\/\/doi.org\/10.1007\/978-3-031-06649-8","DOI":"10.1007\/978-3-031-06649-8"},{"key":"3_CR23","unstructured":"Wang, F., Liu, Y., Liu, K., Wang, Y., Medya, S., Yu, P.S.: Uncertainty in graph neural networks: a survey (2024). https:\/\/arxiv.org\/abs\/2403.07185"},{"issue":"8","key":"3_CR24","doi-asserted-by":"publisher","first-page":"4388","DOI":"10.1109\/TPAMI.2021.3067100","volume":"44","author":"L Zhang","year":"2022","unstructured":"Zhang, L., Bao, C., Ma, K.: Self-distillation: towards efficient and compact neural networks. IEEE Trans. Pattern Anal. Mach. Intell. 44(8), 4388\u20134403 (2022). https:\/\/doi.org\/10.1109\/TPAMI.2021.3067100","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3_CR25","doi-asserted-by":"publisher","unstructured":"Zhang, W., et al.: Reliable data distillation on graph convolutional network. In: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data, pp. 1399\u20131414. SIGMOD \u201920, Association for Computing Machinery, New York, NY, USA (2020). https:\/\/doi.org\/10.1145\/3318464.3389706","DOI":"10.1145\/3318464.3389706"},{"key":"3_CR26","doi-asserted-by":"publisher","first-page":"8154","DOI":"10.1039\/C9SC00616H","volume":"10","author":"Y Zhang","year":"2019","unstructured":"Zhang, Y., Lee, A.A.: Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learning. Chem. Sci. 10, 8154\u20138163 (2019). https:\/\/doi.org\/10.1039\/C9SC00616H","journal-title":"Chem. Sci."},{"key":"3_CR27","doi-asserted-by":"publisher","unstructured":"Zhu, W., Razavian, N.: variationally regularized graph-based representation learning for electronic health records. In: Proceedings of the Conference on Health, Inference, and Learning, pp. 1\u201313. CHIL \u201921, Association for Computing Machinery, New York, NY, USA (2021). https:\/\/doi.org\/10.1145\/3450439.3451855","DOI":"10.1145\/3450439.3451855"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence in Healthcare"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-00656-1_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T08:22:17Z","timestamp":1784967737000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-00656-1_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,20]]},"ISBN":["9783032006554","9783032006561"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-00656-1_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,20]]},"assertion":[{"value":"20 August 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","label":"Disclosure of Interests","group":{"name":"EthicsHeading","label":"Ethics"}},{"value":"AIiH","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on AI in Healthcare","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cambridge","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aiih2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aiih.cc\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}