{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T16:25:26Z","timestamp":1775579126573,"version":"3.50.1"},"reference-count":78,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"11","license":[{"start":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T00:00:00Z","timestamp":1667260800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100002347","name":"Bundesministerium fr Bildung und Forschung","doi-asserted-by":"publisher","award":["01IS18025A"],"award-info":[{"award-number":["01IS18025A"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002347","name":"Bundesministerium fr Bildung und Forschung","doi-asserted-by":"publisher","award":["01IS18037A"],"award-info":[{"award-number":["01IS18037A"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Institute of Information Communications Technology Planning Evaluation","award":["2019-0-00079"],"award-info":[{"award-number":["2019-0-00079"]}]},{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["EXC 2046\/1, project-ID: 390685689"],"award-info":[{"award-number":["EXC 2046\/1, project-ID: 390685689"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2022,11,1]]},"DOI":"10.1109\/tpami.2021.3115452","type":"journal-article","created":{"date-parts":[[2021,9,24]],"date-time":"2021-09-24T21:45:05Z","timestamp":1632519905000},"page":"7581-7596","source":"Crossref","is-referenced-by-count":169,"title":["Higher-Order Explanations of Graph Neural Networks via Relevant Walks"],"prefix":"10.1109","volume":"44","author":[{"given":"Thomas","family":"Schnake","sequence":"first","affiliation":[{"name":"BIFOLD &#x2013; Berlin Institute for the Foundations of Learning and Data, Berlin Institute of Technology (TU Berlin), Berlin, Germany"}]},{"given":"Oliver","family":"Eberle","sequence":"additional","affiliation":[{"name":"BIFOLD &#x2013; Berlin Institute for the Foundations of Learning and Data, Berlin Institute of Technology (TU Berlin), Berlin, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8224-1486","authenticated-orcid":false,"given":"Jonas","family":"Lederer","sequence":"additional","affiliation":[{"name":"Berlin Institute of Technology (TU Berlin), Berlin, Germany"}]},{"given":"Shinichi","family":"Nakajima","sequence":"additional","affiliation":[{"name":"BIFOLD &#x2013; Berlin Institute for the Foundations of Learning and Data, Berlin Institute of Technology (TU Berlin), Berlin, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8342-0964","authenticated-orcid":false,"given":"Kristof T.","family":"Schutt","sequence":"additional","affiliation":[{"name":"BIFOLD &#x2013; Berlin Institute for the Foundations of Learning and Data, Berlin Institute of Technology (TU Berlin), Berlin, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3861-7685","authenticated-orcid":false,"given":"Klaus-Robert","family":"Muller","sequence":"additional","affiliation":[{"name":"Google Research, Brain team, Berlin, BIFOLD &#x2013; Berlin Institute for the Foundations of Learning and Data, the Berlin Institute of Technology (TU Berlin), Berlin, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7243-6186","authenticated-orcid":false,"given":"Gregoire","family":"Montavon","sequence":"additional","affiliation":[{"name":"BIFOLD &#x2013; Berlin Institute for the Foundations of Learning and Data, Berlin Institute of Technology (TU Berlin), Berlin, Germany"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2008.2005605"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1063\/1.5019779"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bty294"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1159"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1209"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1026"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/3326362"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-019-08987-4"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-28954-6"},{"key":"ref11","article-title":"A roadmap for a rigorous science of interpretability","author":"Doshi-Velez","year":"2017","journal-title":"CoRR"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939778"},{"key":"ref13","first-page":"3319","article-title":"Axiomatic attribution for deep networks","volume-title":"Proc. 34th Int. Conf. Mach. Learn.","volume":"70","author":"Sundararajan"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2788613"},{"key":"ref15","first-page":"7786","article-title":"Towards robust interpretability with self-explaining neural networks","volume-title":"Proc. 32nd Int. Conf. Neural Inf. Process. Syst.","author":"Alvarez-Melis"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0130140"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2021.3060483"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2019.12.012"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiopen.2021.01.001"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3020738"},{"issue":"104","key":"ref21","first-page":"1","article-title":"Explaining explanations: Axiomatic feature interactions for deep networks","volume-title":"J. Mach. Learn. Res.","volume":"22","author":"Janizek","year":"2021"},{"key":"ref22","first-page":"1087","article-title":"Learning global pairwise interactions with Bayesian neural networks","volume-title":"Proc. 24th Eur. Conf. Artif. Intell.","volume":"325","author":"Cui"},{"key":"ref23","article-title":"Detecting statistical interactions from neural network weights","volume-title":"Proc. 6th Int. Conf. Learn. Representations","author":"Tsang"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01103"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-5308"},{"key":"ref26","first-page":"9240","article-title":"GNNExplainer: Generating explanations for graph neural networks","volume-title":"Proc. 33rd Int. Conf. Neural Inf. Process. Syst.","author":"Ying"},{"key":"ref27","first-page":"19620","article-title":"Parameterized explainer for graph neural network","volume-title":"Proc. 34th Int. Conf. Neural Inf. Process. Syst.","volume":"33","author":"Luo"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403085"},{"key":"ref29","first-page":"12225","article-title":"PGM-explainer: Probabilistic graphical model explanations for graph neural networks","volume-title":"Proc. 34th Int. Conf. Neural Inf. Process. Syst.","author":"Vu"},{"key":"ref30","article-title":"Interpreting graph neural networks for NLP with differentiable edge masking","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Schlichtkrull"},{"key":"ref31","first-page":"6666","article-title":"Generative causal explanations for graph neural networks","volume-title":"Proc. 38th Int. Conf. Mach. Learn.","volume":"139","author":"Lin"},{"key":"ref32","first-page":"12 241","article-title":"On explainability of graph neural networks via subgraph explorations","volume-title":"Proc. 38th Int. Conf. Mach. Learn.","volume":"139","author":"Yuan"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2022.04.070"},{"key":"ref34","article-title":"GraphLIME: Local interpretable model explanations for graph neural networks","author":"Huang","year":"2020","journal-title":"CoRR"},{"key":"ref35","article-title":"Explainability in graph neural networks: A taxonomic survey","author":"Yuan","year":"2020","journal-title":"CoRR"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.18178\/wcse.2019.06.016"},{"key":"ref37","first-page":"991","article-title":"SchNet: A continuous-filter convolutional neural network for modeling quantum interactions","volume-title":"Proc. 31st Int. Conf. Neural Inf. Process. Syst.","author":"Sch\u00fctt"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/505"},{"key":"ref39","first-page":"1263","article-title":"Neural message passing for quantum chemistry","volume-title":"Proc. 34th Int. Conf. Mach. Learn.","volume":"70","author":"Gilmer"},{"key":"ref40","first-page":"22118","article-title":"Open graph benchmark: Datasets for machine learning on graphs","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Hu"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2016.11.008"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-28954-6_10"},{"key":"ref43","article-title":"Spectral networks and locally connected networks on graphs","volume-title":"Proc. 2nd Int. Conf. Learn. Representations","author":"Bruna"},{"key":"ref44","article-title":"How powerful are graph neural networks?,","volume-title":"Proc. 7th Int. Conf. Learn. Representations","author":"Xu"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.5555\/3157382.3157527"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2017.2693418"},{"key":"ref47","first-page":"1024","article-title":"Inductive representation learning on large graphs","volume-title":"Proc. 31st Int. Conf. Neural Inf. Process. Syst.","author":"Hamilton"},{"key":"ref48","first-page":"2224","article-title":"Convolutional networks on graphs for learning molecular fingerprints","volume-title":"Proc. 28th Int. Conf. Neural Inf. Process. Syst.","author":"Duvenaud"},{"key":"ref49","article-title":"Very deep convolutional networks for large-scale image recognition","volume-title":"Proc. 3rd Int. Conf. Learn. Representations","author":"Simonyan"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.5555\/3327345.3327389"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-28954-6_11"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1103\/RevModPhys.74.47"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2016.2599820"},{"key":"ref54","first-page":"1631","article-title":"Recursive deep models for semantic compositionality over a sentiment treebank","volume-title":"Proc. Conf. Empir. Methods Natural Lang. Process.","author":"Socher"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00009"},{"key":"ref56","article-title":"Not just a black box: Learning important features through propagating activation differences","author":"Shrikumar","year":"2016","journal-title":"CoRR"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-28954-6_13"},{"key":"ref58","first-page":"342","article-title":"The shattered gradients problem: If resnets are the answer, then what is the question?,","volume-title":"Proc. 34th Int. Conf. Mach. Learn.","volume":"70","author":"Balduzzi"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2017.10.011"},{"key":"ref60","first-page":"9525","article-title":"Sanity checks for saliency maps","volume-title":"Proc. 32nd Int. Conf. Neural Inf. Process. Syst.","volume":"31","author":"Adebayo"},{"key":"ref61","first-page":"5898","article-title":"Evaluating attribution for graph neural networks","volume-title":"Proc. 34th Int. Conf. Neural Inf. Process. Syst.","volume":"33","author":"Sanchez-Lengeling"},{"key":"ref62","article-title":"Explanations can be manipulated and geometry is to blame","volume-title":"Proc. 33rd Int. Conf. Neural Inf. Process. Syst.","volume":"32","author":"Dombrowski"},{"key":"ref63","volume-title":"Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition","author":"Jurafsky","year":"2009"},{"issue":"16","key":"ref64","first-page":"555","article-title":"Approximate tree kernels","volume":"11","author":"Rieck","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.2200\/s00416ed1v01y201204hlt016"},{"key":"ref66","first-page":"4349","article-title":"Man is to computer programmer as woman is to homemaker? Debiasing word embeddings","volume-title":"Proc. 30th Int. Conf. Neural Inf. Process. Syst.","author":"Bolukbasi"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N19-1061"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1038\/s41570-020-0189-9"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-physchem-042018-052331"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-40245-7"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1021\/acs.chemrev.1c00107"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1021\/acs.chemrev.0c01111"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1038\/ncomms13890"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jctc.8b00908"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2014.22"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-28954-6_17"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"ref78","article-title":"Inceptionism: Going deeper into neural networks","author":"Mordvintsev","year":"2015"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/9910240\/09547794.pdf?arnumber=9547794","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,11]],"date-time":"2024-01-11T22:52:06Z","timestamp":1705013526000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9547794\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,1]]},"references-count":78,"journal-issue":{"issue":"11"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2021.3115452","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"value":"0162-8828","type":"print"},{"value":"2160-9292","type":"electronic"},{"value":"1939-3539","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,1]]}}}