{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T00:21:04Z","timestamp":1759191664318,"version":"3.44.0"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032060778","type":"print"},{"value":"9783032060785","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T00:00:00Z","timestamp":1759190400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T00:00:00Z","timestamp":1759190400000},"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\/978-3-032-06078-5_7","type":"book-chapter","created":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T18:50:22Z","timestamp":1759171822000},"page":"113-129","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Faithful Explanations for\u00a0Graph Classification Using Logic"],"prefix":"10.1007","author":[{"given":"Alessio","family":"Ragno","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marc","family":"Plantevit","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"C\u00e9line","family":"Robardet","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,30]]},"reference":[{"key":"7_CR1","doi-asserted-by":"crossref","unstructured":"Agarwal, C., Queen, O., Lakkaraju, H., Zitnik, M.: Evaluating explainability for graph neural networks. Sci. Data 10(1) (2023)","DOI":"10.1038\/s41597-023-01974-x"},{"key":"7_CR2","unstructured":"Armgaan, B., Dalmia, M., Medya, S., Ranu, S.: Graphtrail: translating GNN predictions into human-interpretable logical rules. In: NeurIPS (2024)"},{"key":"7_CR3","unstructured":"Azzolin, S., Longa, A., Barbiero, P., Lio, P., Passerini, A.: Global explainability of gnns via logic combination of learned concepts. In: ICLR (2023)"},{"key":"7_CR4","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1016\/j.neucom.2021.04.039","volume":"450","author":"P Bongini","year":"2021","unstructured":"Bongini, P., Bianchini, M., Scarselli, F.: Molecular generative graph neural networks for drug discovery. Neurocomputing 450, 242\u2013252 (2021). Aug","journal-title":"Neurocomputing"},{"key":"7_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2022.103822","volume":"314","author":"G Ciravegna","year":"2023","unstructured":"Ciravegna, G., et al.: Logic explained networks. Artif. Intell. 314, 103822 (2023)","journal-title":"Artif. Intell."},{"key":"7_CR6","unstructured":"Cordella, L.P., Foggia, P., Sansone, C., Vento, M., et\u00a0al.: An improved algorithm for matching large graphs. In: IAPR-TC15 (2001)"},{"key":"7_CR7","doi-asserted-by":"crossref","unstructured":"Dai, E., Wang, S.: Towards self-explainable graph neural network. In: International Conference on Information & Knowledge Management, CIKM, pp. 302\u2013311 (2021)","DOI":"10.1145\/3459637.3482306"},{"key":"7_CR8","doi-asserted-by":"crossref","unstructured":"Darwiche, A.: Logic for explainable ai. In: 2023 38th Annual ACM\/IEEE Symposium on Logic in Computer Science (LICS), pp. 1\u201311. IEEE (2023)","DOI":"10.1109\/LICS56636.2023.10175757"},{"key":"7_CR9","doi-asserted-by":"crossref","unstructured":"Debnath, A.K., Lopez\u00a0de Compadre, R.L., Debnath, G., Shusterman, A.J., Hansch, C.: Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. J. Med. Chem. 34(2), 786\u2013797 (1991)","DOI":"10.1021\/jm00106a046"},{"issue":"4","key":"7_CR10","doi-asserted-by":"publisher","first-page":"771","DOI":"10.1016\/S0022-2836(03)00628-4","volume":"330","author":"PD Dobson","year":"2003","unstructured":"Dobson, P.D., Doig, A.J.: Distinguishing enzyme structures from non-enzymes without alignments. J. Mol. Biol. 330(4), 771\u2013783 (2003)","journal-title":"J. Mol. Biol."},{"key":"7_CR11","doi-asserted-by":"crossref","unstructured":"Duval, A., Malliaros, F.D.: Graphsvx: shapley value explanations for graph neural networks. In: ECML PKDD, pp. 302\u2013318 (2021)","DOI":"10.1007\/978-3-030-86520-7_19"},{"key":"7_CR12","doi-asserted-by":"crossref","unstructured":"Fan, W., Ma, Y., Li, Q., He, Y., Zhao, E., Tang, J., Yin, D.: Graph neural networks for social recommendation. In: WWW 2019. ACM Press (2019)","DOI":"10.1145\/3308558.3313488"},{"key":"7_CR13","doi-asserted-by":"crossref","unstructured":"Jiang, D., et al.: Could graph neural networks learn better molecular representation for drug discovery? J. Cheminform. 13(1) (2021)","DOI":"10.1186\/s13321-020-00479-8"},{"issue":"1","key":"7_CR14","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1021\/jm040835a","volume":"48","author":"J Kazius","year":"2004","unstructured":"Kazius, J., McGuire, R., Bursi, R.: Derivation and validation of toxicophores for mutagenicity prediction. J. Med. Chem. 48(1), 312\u2013320 (2004)","journal-title":"J. Med. Chem."},{"key":"7_CR15","doi-asserted-by":"crossref","unstructured":"Lipton, Z.C.: The mythos of model interpretability: in machine learning, the concept of interpretability is both important and slippery. Queue 16(3), 31\u201357 (2018)","DOI":"10.1145\/3236386.3241340"},{"key":"7_CR16","first-page":"19620","volume":"33","author":"D Luo","year":"2020","unstructured":"Luo, D., et al.: Parameterized explainer for graph neural network. NeurIPS 33, 19620\u201319631 (2020)","journal-title":"NeurIPS"},{"key":"7_CR17","doi-asserted-by":"crossref","unstructured":"Martins, I.F., Teixeira, A.L., Pinheiro, L., Falcao, A.O.: A bayesian approach to in silico blood-brain barrier penetration modeling. J. Chem. Infor. Model. 52(6), 1686\u20131697 (jun 2012)","DOI":"10.1021\/ci300124c"},{"key":"7_CR18","doi-asserted-by":"crossref","unstructured":"Ragno, A., Capobianco, R.: Impo: interpretable memory-based prototypical pooling. In: WSDM, pp. 625\u2013632 (2025)","DOI":"10.1145\/3701551.3703543"},{"key":"7_CR19","unstructured":"Ragno, A., La\u00a0Rosa, B., Capobianco, R.: Prototype-based interpretable graph neural networks. IEEE Trans. Artifi. Intell., 1\u201311 (2022)"},{"key":"7_CR20","doi-asserted-by":"crossref","unstructured":"Ragno, A., Plantevit, M., Robardet, C., Capobianco, R.: Transparent explainable logic layers. In: ECAI. vol. 392, pp. 914\u2013921 (2024)","DOI":"10.3233\/FAIA240579"},{"issue":"5","key":"7_CR21","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":"7_CR22","doi-asserted-by":"crossref","unstructured":"Schwarzenberg, R., H\u00fcbner, M., Harbecke, D., Alt, C., Hennig, L.: Layerwise relevance visualization in convolutional text graph classifiers. In: Workshop on Graph-Based Methods for Natural Language Processing, TextGraphs, pp. 58\u201362 (2019)","DOI":"10.18653\/v1\/D19-5308"},{"key":"7_CR23","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: International Conference on Computer Vision, ICCV, pp. 618\u2013626 (2017)","DOI":"10.1109\/ICCV.2017.74"},{"key":"7_CR24","unstructured":"Sundararajan, M., Taly, A., Yan, Q.: Axiomatic attribution for deep networks. In: ICML, pp. 3319\u20133328. PMLR (2017)"},{"issue":"3","key":"7_CR25","first-page":"347","volume":"14","author":"N Wale","year":"2007","unstructured":"Wale, N., Watson, I.A., Karypis, G.: Comparison of descriptor spaces for chemical compound retrieval and classification. KAIS 14(3), 347\u2013375 (2007)","journal-title":"KAIS"},{"key":"7_CR26","unstructured":"Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? In: ICLR (2019)"},{"key":"7_CR27","unstructured":"Ying, Z., Bourgeois, D., You, J., Zitnik, M., Leskovec, J.: Gnnexplainer: generating explanations for graph neural networks. NeurIPS 32 (2019)"},{"key":"7_CR28","unstructured":"Yuan, H., Yu, H., Wang, J., Li, K., Ji, S.: On explainability of graph neural networks via subgraph explorations. In: ICML, pp. 12241\u201312252. PMLR (2021)"},{"key":"7_CR29","first-page":"19810","volume":"35","author":"S Zhang","year":"2022","unstructured":"Zhang, S., Liu, Y., Shah, N., Sun, Y.: Gstarx: explaining graph neural networks with structure-aware cooperative games. Adv. Neural Inform. Process. Syst. NeurIPS 35, 19810\u201319823 (2022)","journal-title":"Adv. Neural Inform. Process. Syst. NeurIPS"},{"issue":"8","key":"7_CR30","first-page":"9127","volume":"36","author":"Z Zhang","year":"2022","unstructured":"Zhang, Z., Liu, Q., Wang, H., Lu, C., Lee, C.: ProtGNN: towards self-explaining graph neural networks. AAAI Artifi. Intell. 36(8), 9127\u20139135 (2022)","journal-title":"AAAI Artifi. Intell."}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases. Research Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-06078-5_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T18:50:29Z","timestamp":1759171829000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-06078-5_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,30]]},"ISBN":["9783032060778","9783032060785"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-06078-5_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,30]]},"assertion":[{"value":"30 September 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":"Porto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","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":"15 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecmlpkdd.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}