{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,19]],"date-time":"2026-07-19T01:55:56Z","timestamp":1784426156020,"version":"3.55.0"},"reference-count":67,"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":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62172166"],"award-info":[{"award-number":["62172166"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61772366"],"award-info":[{"award-number":["61772366"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62276277"],"award-info":[{"award-number":["62276277"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100021171","name":"Basic and Applied Basic Research Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2022A1515011380"],"award-info":[{"award-number":["2022A1515011380"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100021171","name":"Basic and Applied Basic Research Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2022B1515120059"],"award-info":[{"award-number":["2022B1515120059"]}],"id":[{"id":"10.13039\/501100021171","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":[[2025,5]]},"DOI":"10.1109\/tnnls.2024.3420895","type":"journal-article","created":{"date-parts":[[2024,7,16]],"date-time":"2024-07-16T13:28:35Z","timestamp":1721136515000},"page":"9422-9436","source":"Crossref","is-referenced-by-count":19,"title":["Fine-Grained Learning Behavior-Oriented Knowledge Distillation for Graph Neural Networks"],"prefix":"10.1109","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9447-7111","authenticated-orcid":false,"given":"Kang","family":"Liu","sequence":"first","affiliation":[{"name":"School of Computer Science, South China Normal University, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8659-4062","authenticated-orcid":false,"given":"Zhenhua","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer Science, South China Normal University, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5972-559X","authenticated-orcid":false,"given":"Chang-Dong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Beibei","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Philosophy and Social Development, South China Normal University, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7493-5672","authenticated-orcid":false,"given":"Yunwen","family":"Chen","sequence":"additional","affiliation":[{"name":"Research and Development Department, DataGrand Inc., Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3217090"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2022.3182768"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/3592571.3592976"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI53787.2023.10230380"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3181780"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/s40747-022-00926-z"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2008.2005605"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.01.131"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3539597.3570457"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/3584945"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3615073"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2024.102224"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2024.3370918"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109874"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3183143"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-021-10059-3"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1503.02531"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557381"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3223018"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2023.3333846"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00710"},{"key":"ref22","first-page":"1","article-title":"Learning MLPs on graphs: A unified view of effectiveness, robustness, and efficiency","volume-title":"Proc. ICLR","author":"Tian"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539315"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121671"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539320"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512209"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/3616855.3635768"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01165"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2005.1555942"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.2974943"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2023.3299012"},{"key":"ref33","first-page":"1","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"Proc. ICLR","author":"Kipf"},{"key":"ref34","first-page":"1","article-title":"Graph attention networks","volume-title":"Proc. ICLR","author":"Velickovic"},{"key":"ref35","first-page":"1024","article-title":"Inductive representation learning on large graphs","volume-title":"Proc. NeurIPS","volume":"30","author":"Hamilton"},{"key":"ref36","first-page":"1","article-title":"Transferring knowledge to smaller network with class-distance loss","volume-title":"Proc. ICLR","author":"Kim"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i15.17610"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i8.16865"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2024.3354928"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2022.3141255"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i2.25236"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2023.3325814"},{"key":"ref43","first-page":"1","article-title":"FitNets: Hints for thin deep nets","volume-title":"Proc. ICLR","author":"Romero"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00497"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00409"},{"key":"ref46","first-page":"1","article-title":"Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer","volume-title":"Proc. ICLR","author":"Komodakis"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3450068"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/320"},{"key":"ref49","first-page":"29761","article-title":"Geometric knowledge distillation: Topology compression for graph neural networks","volume-title":"Proc. NeurIPS","volume":"35","author":"Yang"},{"key":"ref50","first-page":"1","article-title":"Graph-less neural networks: Teaching old MLPs new tricks via distillation","volume-title":"Proc. ICLR","author":"Zhang"},{"issue":"1564","key":"ref51","first-page":"37571","article-title":"Quantifying the knowledge in GNNs for reliable distillation into MLPs","volume-title":"Proc. ICML","volume":"202","author":"Wu"},{"key":"ref52","first-page":"1","article-title":"NOSMOG: Learning noise-robust and structure-aware MLPs on graphs","volume-title":"Proc. NeurIPS Workshop, New Frontiers Graph Learn.","author":"Tian"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611977653.ch18"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26232"},{"key":"ref55","first-page":"1","article-title":"VQGraph: Rethinking graph representation space for bridging GNNs and MLPs","volume-title":"Proc. ICLR","author":"Yang"},{"key":"ref56","first-page":"11815","article-title":"Knowledge distillation improves graph structure augmentation for graph neural networks","volume-title":"Proc. NeurIPS","volume":"35","author":"Wu"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i6.25944"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i4.25553"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1214\/009053607000000505"},{"key":"ref60","first-page":"792","article-title":"Learning to classify text from labeled and unlabeled documents","volume-title":"Proc. Nat. Conf. Artif. Intell.","author":"Nigam"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1145\/276675.276685"},{"key":"ref62","first-page":"22118","article-title":"Open graph benchmark: Datasets for machine learning on graphs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Hu"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783381"},{"key":"ref64","first-page":"509","article-title":"Learning to extract symbolic knowledge from the world wide web","volume-title":"Proc. Nat. Conf. Artif. Intell.","author":"Craven"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/2.781637"},{"issue":"721","key":"ref66","first-page":"8026","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Paszke"},{"key":"ref67","article-title":"Deep graph library: A graph-centric, highly-performant package for graph neural networks","author":"Wang","year":"2019","journal-title":"arXiv:1909.01315"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/5962385\/10982361\/10599879.pdf?arnumber=10599879","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T18:39:37Z","timestamp":1764959977000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10599879\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5]]},"references-count":67,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2024.3420895","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5]]}}}