{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,2]],"date-time":"2026-08-02T06:45:58Z","timestamp":1785653158990,"version":"3.56.0"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032316653","type":"print"},{"value":"9783032316660","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T00:00:00Z","timestamp":1785715200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T00:00:00Z","timestamp":1785715200000},"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":[[2027]]},"DOI":"10.1007\/978-3-032-31666-0_24","type":"book-chapter","created":{"date-parts":[[2026,8,2]],"date-time":"2026-08-02T05:47:19Z","timestamp":1785649639000},"page":"360-374","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DotGreedX: Combining Scoring-Based Technique and\u00a0Greedy Search for\u00a0GNN Explainability"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4609-8800","authenticated-orcid":false,"given":"Mariana Brito","family":"Azevedo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1658-0527","authenticated-orcid":false,"given":"Luc","family":"Brun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3509-2609","authenticated-orcid":false,"given":"Pierre","family":"H\u00e9roux","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6493-1769","authenticated-orcid":false,"given":"Jean-Luc","family":"Lamotte","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,3]]},"reference":[{"issue":"1","key":"24_CR1","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1038\/s41597-023-01974-x","volume":"10","author":"C Agarwal","year":"2023","unstructured":"Agarwal, C., Queen, O., Lakkaraju, H., Zitnik, M.: Evaluating explainability for graph neural networks. Sci. Data 10(1), 144 (2023)","journal-title":"Sci. Data"},{"key":"24_CR2","unstructured":"Baldassarre, F., Azizpour, H.: Explainability techniques for graph convolutional networks (2019)"},{"key":"24_CR3","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1007\/978-3-031-94139-9_5","volume-title":"Graph-Based Representations in Pattern Recognition","author":"M Brito Azevedo","year":"2025","unstructured":"Brito Azevedo, M., Brun, L., H\u00e9roux, P., Lamotte, J.L., Bureau, R., Lepailleur, A.: Graph neural network based on molecular and pharmacophoric features for drug design applications. In: Brun, L., Carletti, V., Bougleux, S., Ga\u00fcz\u00e8re, B. (eds.) Graph-Based Representations in Pattern Recognition, pp. 47\u201357. Springer Nature Switzerland, Cham (2025)"},{"key":"24_CR4","unstructured":"Bugue\u00f1o, M., Biswas, R., de\u00a0Melo, G.: Graph-based explainable AI: a comprehensive survey (2024). working paper or preprint"},{"issue":"6","key":"24_CR5","doi-asserted-by":"publisher","first-page":"1011","DOI":"10.1007\/s11633-024-1510-8","volume":"21","author":"E Dai","year":"2024","unstructured":"Dai, E., et al.: A comprehensive survey on trustworthy graph neural networks: privacy, robustness, fairness, and explainability. Mach. Intell. Res. 21(6), 1011\u20131061 (2024)","journal-title":"Mach. Intell. Res."},{"key":"24_CR6","unstructured":"Feng, Q., Liu, N., Yang, F., Tang, R., Du, M., Hu, X.: DEGREE: decomposition based explanation for graph neural networks. In: International Conference on Learning Representations (2022)"},{"issue":"1","key":"24_CR7","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1186\/s40537-023-00876-4","volume":"11","author":"B Khemani","year":"2024","unstructured":"Khemani, B., Patil, S., Kotecha, K., Tanwar, S.: A review of graph neural networks: concepts, architectures, techniques, challenges, datasets, applications, and future directions. J. Big Data 11(1), 18 (2024)","journal-title":"J. Big Data"},{"key":"24_CR8","unstructured":"Li, Y., Zhou, J., Verma, S., Chen, F.: A survey of explainable graph neural networks: taxonomy and evaluation metrics (2023)"},{"key":"24_CR9","unstructured":"Lu, S., Liu, B., Mills, K.G., He, J., Niu, D.: EIG-search: generating edge-induced subgraphs for GNN explanation in linear time. ICML. OpenReview.net (2024)"},{"key":"24_CR10","unstructured":"Luo, D., et al.: Parameterized explainer for graph neural network. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS \u201920, Curran Associates Inc, Red Hook, NY, USA (2020)"},{"key":"24_CR11","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)"},{"key":"24_CR12","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: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10764\u201310773 (2019)","DOI":"10.1109\/CVPR.2019.01103"},{"key":"24_CR13","unstructured":"Sanchez-Lengeling, B., et al.: Evaluating attribution for graph neural networks. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) Advances in Neural Information Processing Systems, vol.\u00a033, pp. 5898\u20135910. Curran Associates, Inc. (2020)"},{"key":"24_CR14","unstructured":"TrustAGI Lab: a repository of benchmark graph datasets for graph classification (2023). https:\/\/github.com\/TrustAGI-Lab\/graph_datasets, gitHub repository. Accessed 05 Dec 2025"},{"key":"24_CR15","unstructured":"Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? In: International Conference on Learning Representations (2019)"},{"key":"24_CR16","unstructured":"Ying, Z., Bourgeois, D., You, J., Zitnik, M., Leskovec, J.: GNNExplainer: generating explanations for graph neural networks. In: Advances in Neural Information Processing Systems, vol. 32, Curran Associates. Inc (2019)"},{"key":"24_CR17","doi-asserted-by":"crossref","unstructured":"Yuan, H., Tang, J., Hu, X., Ji, S.: XGNN: towards model-level explanations of graph neural networks. In: arXiv preprint arXiv:2006.14895, pp. 430\u2013438 (2020)","DOI":"10.1145\/3394486.3403085"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-31666-0_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,2]],"date-time":"2026-08-02T05:47:21Z","timestamp":1785649641000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-31666-0_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,3]]},"ISBN":["9783032316653","9783032316660"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-31666-0_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,3]]},"assertion":[{"value":"3 August 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lyon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 August 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 August 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpr2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icpr2026.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}