{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T20:05:46Z","timestamp":1780949146370,"version":"3.54.1"},"reference-count":61,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"7","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2024YFB3908500"],"award-info":[{"award-number":["2024YFB3908500"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62476274"],"award-info":[{"award-number":["62476274"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U22B2048"],"award-info":[{"award-number":["U22B2048"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62394330"],"award-info":[{"award-number":["62394330"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Special Project of Joint Funding for Municipal"},{"name":"University (Institute) and Enterprise"},{"name":"Guangzhou Basic Research Program","award":["2024A03J0395"],"award-info":[{"award-number":["2024A03J0395"]}]},{"name":"Science and Technology Projects in Guangzhou","award":["2024D03J0010"],"award-info":[{"award-number":["2024D03J0010"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1109\/tpami.2026.3672916","type":"journal-article","created":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T19:58:08Z","timestamp":1774036688000},"page":"8626-8641","source":"Crossref","is-referenced-by-count":0,"title":["Graph Condensation via Homophily Node Refining and Fine-Grained Distribution Matching"],"prefix":"10.1109","volume":"48","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4649-0167","authenticated-orcid":false,"given":"Ruiwen","family":"Yuan","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9333-8200","authenticated-orcid":false,"given":"Yongqiang","family":"Tang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0752-941X","authenticated-orcid":false,"given":"Wensheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3334751"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2024.3508766"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3182052"},{"key":"ref4","first-page":"1025","article-title":"Inductive representation learning on large graphs","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Hamilton"},{"key":"ref5","first-page":"2713","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Kipf"},{"key":"ref6","first-page":"9104","article-title":"How powerful are graph neural networks?","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Xu"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3323376"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3067100"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3696410.3714916"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.52202\/075280-0264"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2025.3535877"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2025.3540787"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/1374376.1374456"},{"key":"ref14","first-page":"2331","article-title":"Principle of relevant information for graph sparsification","volume-title":"Proc. Uncertainty Artif. Intell.","author":"Yu"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/1007352.1007400"},{"key":"ref16","first-page":"16712","article-title":"Graph coarsening with neural networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Cai"},{"issue":"116","key":"ref17","first-page":"1","article-title":"Graph reduction with spectral and cut guarantees","volume":"20","author":"Loukas","year":"2019","journal-title":"J. Mach. Learn. Res."},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2020.100253"},{"key":"ref19","first-page":"24418","article-title":"Graph condensation for graph neural networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Jin"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE60146.2024.00237"},{"key":"ref21","first-page":"60379","article-title":"Navigating complexity: Toward lossless graph condensation via expanding window matching","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhang"},{"key":"ref22","article-title":"Graph condensation via receptive field distribution matching","author":"Liu","year":"2022"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-70344-7_4"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3485691"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645694"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599398"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.52202\/068431-0100"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2024.3459932"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3285215"},{"key":"ref30","first-page":"30702","article-title":"Graph distillation with eigenbasis matching","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Liu"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1002\/jgt.3190130114"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1137\/08074489X"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467256"},{"key":"ref34","first-page":"3237","article-title":"Spectrally approximating large graphs with smaller graphs","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Loukas"},{"key":"ref35","first-page":"7736","article-title":"A unifying framework for spectrum-preserving graph sparsification and coarsening","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Hermsdorff"},{"key":"ref36","first-page":"17953","article-title":"Featured graph coarsening with similarity guarantees","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Kumar"},{"key":"ref37","first-page":"53201","article-title":"Does graph distillation see like vision dataset counterpart?","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Yang"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539429"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110904"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3054304"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1145\/3539597.3570385"},{"key":"ref42","first-page":"7793","article-title":"Beyond homophily in graph neural networks: Current limitations and effective designs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zhu"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1145\/3690624.3709227"},{"key":"ref44","first-page":"6861","article-title":"Simplifying graph convolutional networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Wu"},{"key":"ref45","article-title":"A survey on graph structure learning: Progress and opportunities","author":"Zhu","year":"2021"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2018.2820126"},{"key":"ref47","first-page":"2232","article-title":"Active learning for convolutional neural networks: A core-set approach","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Sener"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3225572"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2024.3416621"},{"key":"ref50","first-page":"22118","article-title":"Open graph benchmark: Datasets for machine learning on graphs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Hu"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1093\/comnet\/cnab014"},{"key":"ref52","first-page":"1979","article-title":"GraphSAINT: Graph sampling based inductive learning method","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Zeng"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553517"},{"key":"ref54","first-page":"2667","article-title":"Predict then propagate: Graph neural networks meet personalized pagerank","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Gasteiger"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1606.09375"},{"key":"ref56","first-page":"14239","article-title":"BernNet: Learning arbitrary graph spectral filters via bernstein approximation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"He"},{"key":"ref57","article-title":"Adaptive universal generalized pagerank graph neural network","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Chien"},{"key":"ref58","first-page":"26342","article-title":"Is homophily a necessity for graph neural networks?","volume-title":"Proc. 10th Int. Conf. Learn. Representations","author":"Ma"},{"key":"ref59","first-page":"18738","article-title":"A critical look at the evaluation of GNNs under heterophily: Are we really making progress?","volume-title":"Proc. 11th Int. Conf. Learn. Representations","author":"Platonov"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1145\/3488560.3498408"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403049"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/34\/11552636\/11449452.pdf?arnumber=11449452","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T19:52:30Z","timestamp":1780948350000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11449452\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":61,"journal-issue":{"issue":"7"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2026.3672916","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":[[2026,7]]}}}