{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T20:17:33Z","timestamp":1783455453856,"version":"3.55.0"},"reference-count":54,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"8","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62376142"],"award-info":[{"award-number":["62376142"]}],"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":["U25A20529"],"award-info":[{"award-number":["U25A20529"]}],"id":[{"id":"10.13039\/501100001809","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":[[2026,8]]},"DOI":"10.1109\/tpami.2026.3682858","type":"journal-article","created":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T19:58:32Z","timestamp":1775851112000},"page":"9312-9322","source":"Crossref","is-referenced-by-count":0,"title":["SCGT: Toward Scalable and Comprehensive Graph Transformer"],"prefix":"10.1109","volume":"48","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1461-2329","authenticated-orcid":false,"given":"Jianqing","family":"Liang","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Southeast University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology, Shanxi University, Taiyuan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinkai","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology, Shanxi University, Taiyuan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5887-9327","authenticated-orcid":false,"given":"Jiye","family":"Liang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, School of Computer and Information Technology, Shanxi University, Taiyuan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3298711"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2024.3400402"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.52202\/068431-1539"},{"key":"ref5","first-page":"3469","article-title":"Structure-aware transformer for graph representation learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Chen","year":"2022"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.52202\/068431-1054"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.52202\/068431-1986"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.52202\/075280-2826"},{"key":"ref9","first-page":"29560","article-title":"Graph external attention enhanced transformer","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Liang","year":"2024"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2024.3445463"},{"key":"ref11","first-page":"9104","article-title":"How powerful are graph neural networks?","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Xu","year":"2019"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33014602"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5747"},{"key":"ref15","first-page":"12344","article-title":"Graph neural networks exponentially lose expressive power for node classification","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Oono","year":"2020"},{"key":"ref16","first-page":"14048","article-title":"On the bottleneck of graph neural networks and its practical implications","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Alon","year":"2021"},{"key":"ref17","first-page":"23321","article-title":"Graph inductive biases in transformers without message passing","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ma","year":"2023"},{"key":"ref18","first-page":"1060","article-title":"Rethinking attention with performers","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Choromanski","year":"2021"},{"key":"ref19","first-page":"3962","article-title":"From block-Toeplitz matrices to differential equations on graphs: Towards a general theory for scalable masked transformers","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Choromanski","year":"2022"},{"key":"ref20","first-page":"28877","article-title":"Do transformers really perform badly for graph representation?","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Ying","year":"2021"},{"key":"ref21","first-page":"14245","article-title":"Graph neural networks with learnable structural and positional representations","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Dwivedi","year":"2022"},{"key":"ref22","article-title":"A generalization of transformer networks to graphs","volume-title":"AAAI Workshop Deep Learn. Graphs: Methods Appl.","author":"Dwivedi","year":"2021"},{"key":"ref23","first-page":"21618","article-title":"Rethinking graph transformers with spectral attention","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Kreuzer","year":"2021"},{"key":"ref24","first-page":"4465","article-title":"Distance encoding: Design provably more powerful neural networks for graph representation learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Li","year":"2020"},{"key":"ref25","first-page":"748","article-title":"Directional graph networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Beaini","year":"2021"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539296"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3381125"},{"key":"ref28","first-page":"5156","article-title":"Transformers are RNNs: Fast autoregressive transformers with linear attention","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Katharopoulos","year":"2020"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2023\/523"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.52202\/079017-1821"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00548"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1137\/s1064827595287997"},{"key":"ref33","first-page":"1868","article-title":"Rethinking the expressive power of GNNs via graph biconnectivity","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Zhang","year":"2023"},{"key":"ref34","first-page":"4379","article-title":"Equivariant subgraph aggregation networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Bevilacqua","year":"2022"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.122653799"},{"key":"ref36","first-page":"2713","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Kipf","year":"2017"},{"key":"ref37","article-title":"Residual gated graph convnets","author":"Bresson","year":"2017"},{"key":"ref38","article-title":"Graph attention networks","volume-title":"Int. Conf. Learn. Representations","author":"Veli\u010dkovi\u0107","year":"2018"},{"key":"ref39","first-page":"11265","article-title":"Strategies for pre-training graph neural networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Hu","year":"2020"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1145\/1557019.1557108"},{"key":"ref41","article-title":"New benchmarks for learning on non-homophilous graphs","volume-title":"Proc. Workshop Graph Learn. Benchmarks Web Conf.","author":"Lim","year":"2021"},{"key":"ref42","first-page":"18738","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","year":"2023"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411866"},{"key":"ref44","first-page":"22118","article-title":"Open graph benchmark: Datasets for machine learning on graphs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Hu","year":"2020"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783381"},{"key":"ref46","first-page":"6861","article-title":"Simplifying graph convolutional networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Wu","year":"2019"},{"key":"ref47","first-page":"5453","article-title":"Representation learning on graphs with jumping knowledge networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Xu","year":"2018"},{"key":"ref48","first-page":"2667","article-title":"Predict then propagate: Graph neural networks meet personalized pagerank","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Gasteiger","year":"2019"},{"key":"ref49","article-title":"SIGN: Scalable inception graph neural networks","volume-title":"Proc. Graph Representation Learn. Beyond Workshop Int. Conf. Mach. Learn.","author":"Frasca","year":"2020"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-022-00890-9"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583464"},{"key":"ref52","first-page":"17375","article-title":"GOAT: A global transformer on large-scale graphs","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Kong","year":"2023"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2023\/244"},{"issue":"11","key":"ref54","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/34\/11595778\/11478808.pdf?arnumber=11478808","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T19:45:11Z","timestamp":1783453511000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11478808\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":54,"journal-issue":{"issue":"8"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2026.3682858","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":[[2026,8]]}}}