{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T05:49:59Z","timestamp":1771912199343,"version":"3.50.1"},"reference-count":89,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"5","license":[{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Key Scientific Technological Innovation Research Project of the Ministry of Education"},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U22B2054"],"award-info":[{"award-number":["U22B2054"]}],"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":["62076192"],"award-info":[{"award-number":["62076192"]}],"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":["62276199"],"award-info":[{"award-number":["62276199"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"111 Project","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Program for Cheung Kong Scholars and Innovative Research Team in University","award":["IRT 15R53"],"award-info":[{"award-number":["IRT 15R53"]}]},{"name":"Science and Technology Innovation Project from the Chinese Ministry of Education"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2025,5]]},"DOI":"10.1109\/tnnls.2024.3440269","type":"journal-article","created":{"date-parts":[[2024,9,17]],"date-time":"2024-09-17T18:52:01Z","timestamp":1726599121000},"page":"8470-8484","source":"Crossref","is-referenced-by-count":2,"title":["Automatic Graph Topology-Aware Transformer"],"prefix":"10.1109","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1684-3486","authenticated-orcid":false,"given":"Chao","family":"Wang","sequence":"first","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Xidian University, Xi&#x2019;an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2827-0681","authenticated-orcid":false,"given":"Jiaxuan","family":"Zhao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Xidian University, Xi&#x2019;an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6130-2518","authenticated-orcid":false,"given":"Lingling","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Xidian University, Xi&#x2019;an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3354-9617","authenticated-orcid":false,"given":"Licheng","family":"Jiao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Xidian University, Xi&#x2019;an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5669-9354","authenticated-orcid":false,"given":"Fang","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Xidian University, Xi&#x2019;an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4796-5737","authenticated-orcid":false,"given":"Shuyuan","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Xidian University, Xi&#x2019;an, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2807452"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/MCI.2022.3222050"},{"issue":"89","key":"ref3","first-page":"1","article-title":"Machine learning on graphs: A model and comprehensive taxonomy","volume":"23","author":"Chami","year":"2022","journal-title":"J. Mach. Learn. Res."},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3183143"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2023.3316628"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00936"},{"key":"ref9","first-page":"5453","article-title":"Representation learning on graphs with jumping knowledge networks","volume-title":"Proc. Int. Conf. Mach. Learn.","volume":"80","author":"Xu"},{"key":"ref10","first-page":"1725","article-title":"Simple and deep graph convolutional networks","volume-title":"Proc. 37th Int. Conf. Mach. Learn.","volume":"119","author":"Chen"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512185"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/3530811"},{"key":"ref13","article-title":"When large language models meet evolutionary algorithms","author":"Chao","year":"2024","journal-title":"arXiv:2401.10510"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01402"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00064"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.03.005"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2024.3374070"},{"key":"ref18","article-title":"Attending to graph transformers","author":"M\u00fcller","year":"2024","journal-title":"Trans. Mach. Learn. Res."},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3169488"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3262937"},{"key":"ref21","first-page":"12559","article-title":"Self-supervised graph transformer on large-scale molecular data","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Rong"},{"key":"ref22","first-page":"1","article-title":"Novel positional encodings to enable tree-based transformers","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Shiv"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3532031"},{"key":"ref24","article-title":"Transformer for graphs: An overview from architecture perspective","author":"Min","year":"2022","journal-title":"arXiv:2202.08455"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/637"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2023.3255263"},{"key":"ref27","article-title":"Automated graph machine learning: Approaches, libraries, benchmarks and directions","author":"Wang","year":"2022","journal-title":"arXiv:2201.01288"},{"key":"ref28","first-page":"1","article-title":"AutoGT: Automated graph transformer architecture search","volume-title":"Proc. 11th Int. Conf. Learn. Represent.","author":"Zhang"},{"key":"ref29","first-page":"54","article-title":"NAS-bench-graph: Benchmarking graph neural architecture search","volume-title":"Proc. 36th Conf. Neural Inf. Process. Syst. Datasets Benchmarks Track","author":"Qin"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/195"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.3389\/fdata.2022.1029307"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-022-04096-w"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/3449639.3459318"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/BigData50022.2020.9378060"},{"key":"ref35","first-page":"1","article-title":"Hierarchical graph representation learning with differentiable pooling","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Ying"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482285"},{"key":"ref37","first-page":"1","article-title":"Neural architecture search with reinforcement learning","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Zoph"},{"key":"ref38","article-title":"Simplifying architecture search for graph neural network","author":"Zhao","year":"2020","journal-title":"arXiv:2008.11652"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3178153"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3239842"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3100554"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3220699"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2023.3269816"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.108752"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2022.3151895"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3250264"},{"key":"ref47","first-page":"1","article-title":"DARTS: Differentiable architecture search","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Liu"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE51399.2021.00054"},{"key":"ref49","first-page":"16860","article-title":"Graph differentiable architecture search with structure learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Qin"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1145\/3584945"},{"key":"ref51","first-page":"7968","article-title":"Large-scale graph neural architecture search","volume-title":"Proc. 39th Int. Conf. Mach. Learn.","author":"Guan"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/TETCI.2023.3249255"},{"key":"ref53","first-page":"21618","article-title":"Rethinking graph transformers with spectral attention","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Kreuzer"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539296"},{"key":"ref55","first-page":"28877","article-title":"Do transformers really perform badly for graph representation?","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Ying"},{"key":"ref56","first-page":"3469","article-title":"Structure-aware transformer for graph representation learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Chen"},{"key":"ref57","first-page":"14501","article-title":"Recipe for a general, powerful, scalable graph transformer","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"35","author":"Ramp\u00e1\u0161ek"},{"key":"ref58","first-page":"17375","article-title":"GOAT: A global transformer on large-scale graphs","volume-title":"Proc. 40th Int. Conf. Mach. Learn.","volume":"202","author":"Kong"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref60","first-page":"1","article-title":"Representing long-range context for graph neural networks with global attention","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Jain"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01270"},{"key":"ref62","article-title":"Graph-BERT: Only attention is needed for learning graph representations","author":"Zhang","year":"2020","journal-title":"arXiv:2001.05140"},{"key":"ref63","article-title":"A generalization of transformer networks to graphs","author":"Prakash Dwivedi","year":"2020","journal-title":"arXiv:2012.09699"},{"key":"ref64","article-title":"Gophormer: Ego-graph transformer for node classification","author":"Zhao","year":"2021","journal-title":"arXiv:2110.13094"},{"key":"ref65","first-page":"1","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Kipf"},{"key":"ref66","first-page":"1","article-title":"Inductive representation learning on large graphs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Hamilton"},{"key":"ref67","first-page":"1","article-title":"Graph attention networks","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Velickovic"},{"key":"ref68","first-page":"1","article-title":"How attentive are graph attention networks?","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Brody"},{"key":"ref69","first-page":"1","article-title":"How powerful are graph neural networks?","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Xu"},{"key":"ref70","article-title":"Fast graph representation learning with PyTorch geometric","author":"Fey","year":"2019","journal-title":"arXiv:1903.02428"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3151160"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1109\/4235.585893"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01203"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.5555\/1953048.2078195"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-74640-7"},{"key":"ref76","first-page":"1","article-title":"Geom-GCN: Geometric graph convolutional networks","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Pei"},{"key":"ref77","first-page":"7793","article-title":"Beyond homophily in graph neural networks: Current limitations and effective designs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Zhu"},{"key":"ref78","first-page":"1","article-title":"GraphSAINT: Graph sampling based inductive learning method","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Zeng"},{"key":"ref79","first-page":"22118","article-title":"Open graph benchmark: Datasets for machine learning on graphs","volume-title":"Proc. Conf. Neural Inf. Process. Syst. (NeurIPS)","volume":"33","author":"Hu"},{"issue":"43","key":"ref80","first-page":"1","article-title":"Benchmarking graph neural networks","volume":"24","author":"Dwivedi","year":"2023","journal-title":"J. Mach. Learn. Res."},{"key":"ref81","article-title":"Residual gated graph ConvNets","author":"Bresson","year":"2017","journal-title":"arXiv:1711.07553"},{"key":"ref82","first-page":"13260","article-title":"Principal neighbourhood aggregation for graph nets","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Corso"},{"key":"ref83","first-page":"748","article-title":"Directional graph networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Beani"},{"key":"ref84","first-page":"2625","article-title":"Weisfeiler and lehman go cellular: Cw networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Bodnar"},{"key":"ref85","article-title":"Walking out of the weisfeiler Leman hierarchy: Graph learning beyond message passing","author":"T\u00f6nshoff","year":"2021","journal-title":"arXiv:2102.08786"},{"key":"ref86","first-page":"1","article-title":"From stars to subgraphs: Uplifting any GNN with local structure awareness","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Zhao"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109874"},{"key":"ref88","article-title":"Graph neural networks for graphs with heterophily: A survey","author":"Zheng","year":"2022","journal-title":"arXiv:2202.07082"},{"key":"ref89","first-page":"1","article-title":"Affinity-based homophily: Can we measure homophily of a graph without using node labels?","volume-title":"Proc. ICLR","author":"Ojha"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/5962385\/10982361\/10681642.pdf?arnumber=10681642","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T17:46:30Z","timestamp":1751046390000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10681642\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5]]},"references-count":89,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2024.3440269","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5]]}}}