{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T14:29:10Z","timestamp":1770733750932,"version":"3.49.0"},"reference-count":67,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100004826","name":"Beijing Natural Science Foundation","doi-asserted-by":"publisher","award":["L242167"],"award-info":[{"award-number":["L242167"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U24A20252"],"award-info":[{"award-number":["U24A20252"]}],"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":["62327808"],"award-info":[{"award-number":["62327808"]}],"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":["12531019"],"award-info":[{"award-number":["12531019"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key Laboratory of Human-Machine Hybrid Augmented Intelligence"},{"name":"Xi’an Jiaotong University","award":["HMHAI-202412"],"award-info":[{"award-number":["HMHAI-202412"]}]},{"name":"Key Research and Development Program of Shaanxi Province of China","award":["2024PT-ZCK-66"],"award-info":[{"award-number":["2024PT-ZCK-66"]}]},{"name":"Key Research and Development Program of Shaanxi Province of China","award":["2024CY2-GJHX-48"],"award-info":[{"award-number":["2024CY2-GJHX-48"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1109\/tpami.2025.3630605","type":"journal-article","created":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T18:10:09Z","timestamp":1762539009000},"page":"2236-2252","source":"Crossref","is-referenced-by-count":0,"title":["Graph Quality Matters on Revealing the Semantics Behind the Data in Physical World"],"prefix":"10.1109","volume":"48","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-1409-6419","authenticated-orcid":false,"given":"Jielong","family":"Yan","sequence":"first","affiliation":[{"name":"School of Software, BNRist, THUIBCS, BLBCI, Tsinghua University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9423-0146","authenticated-orcid":false,"given":"Shihui","family":"Ying","sequence":"additional","affiliation":[{"name":"Department of Mathematics, School of Science, Shanghai University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7092-0596","authenticated-orcid":false,"given":"Shaoyi","family":"Du","sequence":"additional","affiliation":[{"name":"Institute of Artificial Intelligence and Robotics, College of Artificial Intelligence, Xi’an Jiaotong University, Xi’an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4971-590X","authenticated-orcid":false,"given":"Yue","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Software, BNRist, THUIBCS, BLBCI, Tsinghua University, Beijing, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-020-2188-x"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2011.08.047"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1126\/science.aaw6718"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1038\/s41582-022-00753-3"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1912328117"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1126\/science.aao2998"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-019-1507-6"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.aax7310"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1093\/acprof:oso\/9780199206650.001.0001"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1038\/35065725"},{"key":"ref11","volume-title":"The Structure and Dynamics of Networks","author":"Newman","year":"2006"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-021-01255-8"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1038\/s43588-021-00098-9"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-021-22197-x"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-021-23774-w"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.24383"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.25369"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1037\/e400002009-005"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2015.2493549"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403201"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1126\/science.aad9029"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.physrep.2016.09.002"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2020.124289"},{"key":"ref24","article-title":"DropEdge: Towards deep graph convolutional networks on node classification","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Rong"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11782"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1002\/cplx.20382"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1002\/cplx.21383"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1038\/s41562-018-0321-8"},{"key":"ref29","first-page":"996","article-title":"MI-GCN: Node mutual information-based graph convolutional network","volume-title":"Proc. Web Conf.","author":"Lei"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3170249"},{"key":"ref31","first-page":"21","article-title":"MixHop: Higher-order graph convolutional architectures via sparsified neighborhood mixing","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Abu-El-Haija"},{"key":"ref32","first-page":"5449","article-title":"Representation learning on graphs with jumping knowledge networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Xu"},{"key":"ref33","first-page":"7793","article-title":"Beyond homophily in graph neural networks: Current limitations and effective designs","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Zhu"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17332"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539315"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3511923"},{"key":"ref37","article-title":"Accurate and scalable graph neural networks via message invariance","volume-title":"Proc. 13th Int. Conf. Learn. Representations","author":"Shi"},{"key":"ref38","article-title":"LMC: Fast training of GNNs via subgraph sampling with provable convergence","volume-title":"Proc. 11th Int. Conf. Learn. Representations","author":"Shi"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i03.5701"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.95.042317"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1002\/j.1538-7305.1948.tb01338.x"},{"key":"ref42","article-title":"FoSR: First-order spectral rewiring for addressing oversquashing in GNNs","volume-title":"Proc. 11th Int. Conf. Learn. Representations","author":"Karhadkar"},{"key":"ref43","article-title":"Understanding oversquashing in GNNs through the lens of effective resistance","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Black"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3615081"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v29i3.2157"},{"key":"ref46","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":"ref47","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Kipf"},{"key":"ref48","article-title":"TADPOLE challenge: Prediction of longitudinal evolution in Alzheimer\u2019s disease","author":"Marinescu","year":"2018"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1517\/14728222.2016.1135132"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkv1507"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1145\/3289600.3291015"},{"key":"ref52","article-title":"A critical look at the evaluation of GNNs under heterophily: Are we really making progress?","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Platonov"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313562"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-97-2242-6_24"},{"key":"ref55","first-page":"20887","article-title":"Large scale learning on non-homophilous graphs: New benchmarks and strong simple methods","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Lim"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1145\/3575637.3575646"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1145\/3732282"},{"key":"ref58","article-title":"Diffusion improves graph learning","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Johannes"},{"key":"ref59","article-title":"Robust graph representation learning via neural sparsification","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zheng"},{"key":"ref60","article-title":"Graph condensation for graph neural networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Jin"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2025.3586369"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3397692"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671765"},{"key":"ref64","article-title":"Graph attention networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Veli\u010dkovi\u0107"},{"key":"ref65","first-page":"1025","article-title":"Inductive representation learning on large graphs","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Hamilton"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM54844.2022.00169"},{"key":"ref67","article-title":"Graph neural networks for graphs with heterophily: A survey","author":"Zheng","year":"2022"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/34\/11372200\/11235636.pdf?arnumber=11235636","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T21:05:33Z","timestamp":1770671133000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11235636\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3]]},"references-count":67,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2025.3630605","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":[[2026,3]]}}}