{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T00:34:54Z","timestamp":1787013294476,"version":"build-2736575974"},"reference-count":39,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"4","license":[{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"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":["62176227"],"award-info":[{"award-number":["62176227"]}],"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":["U2066213"],"award-info":[{"award-number":["U2066213"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1109\/tnnls.2022.3144343","type":"journal-article","created":{"date-parts":[[2022,2,15]],"date-time":"2022-02-15T15:37:41Z","timestamp":1644939461000},"page":"4593-4606","source":"Crossref","is-referenced-by-count":13,"title":["Two-Level Graph Neural Network"],"prefix":"10.1109","volume":"35","author":[{"given":"Xing","family":"Ai","sequence":"first","affiliation":[{"name":"School of Informatics, Xiamen University, Xiamen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengyu","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Informatics, Xiamen University, Xiamen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0542-0640","authenticated-orcid":false,"given":"Zhihong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Informatics, Xiamen University, Xiamen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4496-2028","authenticated-orcid":false,"given":"Edwin R.","family":"Hancock","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of York, York, U.K."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2008.2005605"},{"key":"ref2","first-page":"4821","article-title":"Graph attention networks","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Veli\u010dkovi\u0107"},{"key":"ref3","article-title":"How powerful are graph neural networks?","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Xu"},{"key":"ref4","first-page":"3419","article-title":"Generalization and representational limits of graph neural networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Garg"},{"key":"ref5","first-page":"4081","article-title":"Approximation ratios of graph neural networks for combinatorial problems","volume-title":"Proc. 33rd Int. Conf. Neural Inf. Process. Syst.","author":"Sato"},{"key":"ref6","article-title":"Directional message passing for molecular graphs","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Klicpera"},{"key":"ref7","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"25","author":"Krizhevsky"},{"key":"ref8","article-title":"3D steerable CNNs: Learning rotationally equivariant features in volumetric data","volume-title":"arXiv:1807.02547","author":"Weiler","year":"2018"},{"key":"ref9","first-page":"2224","article-title":"Convolutional networks on graphs for learning molecular fingerprints","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Duvenaud"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bth919"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-17316-5_39"},{"key":"ref12","article-title":"Ripple walk training: A subgraph-based training framework for large and deep graph neural network","volume-title":"arXiv:2002.07206","author":"Bai","year":"2020"},{"key":"ref13","first-page":"8017","article-title":"Subgraph neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Alsentzer"},{"key":"ref14","first-page":"1024","article-title":"Inductive representation learning on large graphs","volume-title":"Advances in Neural Information Processing Systems","author":"Hamilton","year":"2017"},{"key":"ref15","article-title":"EdGNN: A simple and powerful GNN for directed labeled graphs","volume-title":"arXiv:1904.08745","author":"Jaume","year":"2019"},{"key":"ref16","first-page":"1","article-title":"Weisfeiler\u2013Lehman graph kernels","volume":"1","author":"Shervashidze","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2014.05.002"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2018.02.173"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2005.132"},{"key":"ref20","first-page":"488","article-title":"Efficient graphlet kernels for large graph comparison","volume-title":"Proc. Artif. Intell. Statist.","author":"Shervashidze"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/2488388.2488502"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-71681-5_7"},{"key":"ref23","first-page":"9240","article-title":"GNNExplainer: Generating explanations for graph neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Ying"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/S0020-0190(00)00047-8"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TCBB.2006.51"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.tcs.2004.05.009"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1021\/jm00106a046"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btg130"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN48605.2020.9206723"},{"issue":"2","key":"ref30","first-page":"29","article-title":"Protein function prediction via graph kernels","volume":"6","author":"Lab","year":"1990","journal-title":"Oral Radiol."},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1002\/chin.200405239"},{"key":"ref32","first-page":"9859","article-title":"Learning metrics for persistence-based summaries and applications for graph classification","volume-title":"Proc. 33rd Int. Conf. Neural Inf. Process. Syst.","author":"Zhao"},{"key":"ref33","article-title":"A simple yet effective baseline for non-attributed graph classification","volume-title":"arXiv:1811.03508","author":"Cai","year":"2018"},{"key":"ref34","first-page":"291","article-title":"Subgraph matching kernels for attributed graphs","volume-title":"Proc. 29th Int. Conf. Int. Conf. Mach. Learn.","author":"Kriege"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783417"},{"key":"ref36","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Kipf"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219890"},{"key":"ref38","first-page":"16211","article-title":"Random walk graph neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Nikolentzos"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5987"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5962385\/10492491\/09714153.pdf?arnumber=9714153","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T13:45:35Z","timestamp":1728308735000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9714153\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4]]},"references-count":39,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2022.3144343","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4]]}}}