{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:47:34Z","timestamp":1742914054744,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031236174"},{"type":"electronic","value":"9783031236181"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-23618-1_31","type":"book-chapter","created":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T07:05:49Z","timestamp":1675062349000},"page":"467-482","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Improving the\u00a0Quality of\u00a0Rule-Based GNN Explanations"],"prefix":"10.1007","author":[{"given":"Ataollah","family":"Kamal","sequence":"first","affiliation":[]},{"given":"Elouan","family":"Vincent","sequence":"additional","affiliation":[]},{"given":"Marc","family":"Plantevit","sequence":"additional","affiliation":[]},{"given":"C\u00e9line","family":"Robardet","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,1,31]]},"reference":[{"key":"31_CR1","unstructured":"Baldassarre, F., Azizpour, H.: Explainability for GCNs. arXiv:1905.13686 (2019)"},{"key":"31_CR2","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1613\/jair.1.12228","volume":"70","author":"N Burkart","year":"2021","unstructured":"Burkart, N., Huber, M.F.: A survey on the explainability of supervised machine learning. JAIR 70, 245\u2013317 (2021)","journal-title":"JAIR"},{"key":"31_CR3","doi-asserted-by":"crossref","unstructured":"De Bie, T.: An information theoretic framework for data mining. In: SIGKDD, pp. 564\u2013572 (2011)","DOI":"10.1145\/2020408.2020497"},{"key":"31_CR4","doi-asserted-by":"crossref","unstructured":"Duval, A., Malliaros, F.D.: Graphsvx: shapley value explanations for graph neural networks. In: ECMLPKDD2021, pp. 302\u2013318 (2021)","DOI":"10.1007\/978-3-030-86520-7_19"},{"key":"31_CR5","doi-asserted-by":"crossref","unstructured":"F\u00fcrnkranz, J., Kliegr, T., Paulheim, H.: On cognitive preferences and the plausibility of rule-based models. Mach. Learn. 109(4), 853\u2013898 (2019)","DOI":"10.1007\/s10994-019-05856-5"},{"key":"31_CR6","unstructured":"Huang, Q., Yamada, M., Tian, Y., et al.: GraphLIME: local interpretable model explanations for graph neural networks. arXiv:2001.06216 (2020)"},{"key":"31_CR7","unstructured":"Kipf, T., Welling, M.: Semi-supervised classification with GCN. In: ICLR (2017)"},{"key":"31_CR8","doi-asserted-by":"crossref","unstructured":"Lemmerich, F., Becker, M.: pysubgroup: easy-to-use subgroup discovery in python. In: ECMLPKDD, pp. 658\u2013662 (2018)","DOI":"10.1007\/978-3-030-10997-4_46"},{"key":"31_CR9","unstructured":"Luo, D., et al.: Parameterized explainer for GNN. In: NeurIPS 2020 (2020)"},{"issue":"4","key":"31_CR10","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1007\/s10208-011-9093-5","volume":"11","author":"F M\u00e9moli","year":"2011","unstructured":"M\u00e9moli, F.: Gromov-wasserstein distances and the metric approach to object matching. Found. Comput. Math. 11(4), 417\u2013487 (2011)","journal-title":"Found. Comput. Math."},{"key":"31_CR11","unstructured":"Molnar, C.: Interpretable machine learning. Lulu.com (2020)"},{"key":"31_CR12","unstructured":"Morris, C., Kriege, N.M., Bause, F., Kersting, K., Mutzel, P., Neumann, M.: Tudataset: a collection of benchmark datasets for learning with graphs (2020)"},{"key":"31_CR13","doi-asserted-by":"crossref","unstructured":"Olah, C., Mordvintsev, A., Schubert, L.: Feature visualization. Distill 2(11), 46832 (2017)","DOI":"10.23915\/distill.00007"},{"key":"31_CR14","doi-asserted-by":"crossref","unstructured":"Park, H., Neville, J.: Exploiting interaction links for node classification with deep graph neural networks. In: IJCAI 2019, pp. 3223\u20133230 (2019)","DOI":"10.24963\/ijcai.2019\/447"},{"key":"31_CR15","unstructured":"Pope, P.E., Kolouri, S., Rostami, M., Martin, C.E., Hoffmann, H.: Explainability methods for GCN. In: IEEE CVPR, pp. 10772\u201310781 (2019)"},{"key":"31_CR16","unstructured":"Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks. In: ICLR 2014 (2014)"},{"key":"31_CR17","unstructured":"Vayer, T., Courty, N., Tavenard, R., Chapel, L., Flamary, R.: Optimal transport for structured data with application on graphs. In: ICML, pp. 6275\u20136284 (2019)"},{"key":"31_CR18","doi-asserted-by":"crossref","unstructured":"Veyrin-Forrer, L., Kamal, A., Duffner, S., Plantevit, M., Robardet, C.: On GNN explanability with activation patterns, working paper or preprint (2021)","DOI":"10.1007\/s10618-022-00870-z"},{"key":"31_CR19","volume-title":"What does my GNN really capture?","author":"L Veyrin-Forrer","year":"2022","unstructured":"Veyrin-Forrer, L., Kamal, A., Duffner, S., Plantevit, M., Robardet, C.: What does my GNN really capture? IJCAI-ECAI, On the exploration of GNN internal representations. In (2022)"},{"key":"31_CR20","unstructured":"Vu, M.N., Thai, M.T.: PGM-Explainer: probabilistic graphical model explanations for graph neural networks. In: NeurIPS 2020 (2020)"},{"key":"31_CR21","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1039\/C7SC02664A","volume":"9","author":"Z Wu","year":"2018","unstructured":"Wu, Z., et al.: Moleculenet: a benchmark for molecular machine learning. Chem. Sci. 9, 513\u2013530 (2018)","journal-title":"Chem. Sci."},{"key":"31_CR22","doi-asserted-by":"crossref","unstructured":"Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on GNNs. IEEE Trans. NN and Learn. Syst. 32(1), 4\u201324 (2020)","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"31_CR23","unstructured":"Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are GNN? In: ICLR (2019)"},{"key":"31_CR24","unstructured":"Ying, Z., Bourgeois, D., You, J., Zitnik, M., Leskovec, J.: GNNExplainer: generating explanations for graph neural networks. In: NeurIPS, pp. 9240\u20139251 (2019)"},{"key":"31_CR25","doi-asserted-by":"crossref","unstructured":"Yuan, H., Tang, J., Hu, X., Ji, S.: XGNN: towards model-level explanations of graph neural networks. In: KDD2020, pp. 430\u2013438 (2020)","DOI":"10.1145\/3394486.3403085"},{"key":"31_CR26","unstructured":"Yuan, H., Yu, H., Gui, S., Ji, S.: Explainability in graph neural networks: a taxonomic survey. arXiv:2012.15445 (2020)"},{"key":"31_CR27","doi-asserted-by":"crossref","unstructured":"Zhang, M., Cui, Z., Neumann, M., Chen, Y.: An end-to-end deep learning architecture for graph classification. In: AAAI-2018, pp. 4438\u20134445 (2018)","DOI":"10.1609\/aaai.v32i1.11782"}],"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\/978-3-031-23618-1_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T07:15:57Z","timestamp":1675062957000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-23618-1_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031236174","9783031236181"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-23618-1_31","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"31 January 2023","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":"Grenoble","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2022.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1060","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"236","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"22% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3-4","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3-4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"17 demo track papers have been accepted from 28 submissions","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}