{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T00:06:34Z","timestamp":1780358794225,"version":"3.54.1"},"reference-count":74,"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\/501100001348","name":"Agency for Science, Technology and Research (A*STAR) under its Advanced Manufacturing and Engineering (AME) Programmatic Funds","doi-asserted-by":"publisher","award":["A19E3b0099"],"award-info":[{"award-number":["A19E3b0099"]}],"id":[{"id":"10.13039\/501100001348","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001348","name":"Agency for Science, Technology and Research (A*STAR) under its Advanced Manufacturing and Engineering (AME) Programmatic Funds","doi-asserted-by":"publisher","award":["A20H6b0151"],"award-info":[{"award-number":["A20H6b0151"]}],"id":[{"id":"10.13039\/501100001348","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.2022.3223018","type":"journal-article","created":{"date-parts":[[2022,12,2]],"date-time":"2022-12-02T20:55:50Z","timestamp":1670014550000},"page":"4656-4667","source":"Crossref","is-referenced-by-count":46,"title":["On Representation Knowledge Distillation for Graph Neural Networks"],"prefix":"10.1109","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4722-1815","authenticated-orcid":false,"given":"Chaitanya K.","family":"Joshi","sequence":"first","affiliation":[{"name":"Technology and Research (A*STAR), Institute for Infocomm Research, Agency for Science, Fusionopolis, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6649-7660","authenticated-orcid":false,"given":"Fayao","family":"Liu","sequence":"additional","affiliation":[{"name":"Technology and Research (A*STAR), Institute for Infocomm Research, Agency for Science, Fusionopolis, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xu","family":"Xun","sequence":"additional","affiliation":[{"name":"Technology and Research (A*STAR), Institute for Infocomm Research, Agency for Science, Fusionopolis, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8971-0660","authenticated-orcid":false,"given":"Jie","family":"Lin","sequence":"additional","affiliation":[{"name":"Technology and Research (A*STAR), Institute for Infocomm Research, Agency for Science, Fusionopolis, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4748-5792","authenticated-orcid":false,"given":"Chuan Sheng","family":"Foo","sequence":"additional","affiliation":[{"name":"Technology and Research (A*STAR), Institute for Infocomm Research, Agency for Science, Fusionopolis, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"Proc. ICLR","author":"Kipf"},{"key":"ref2","first-page":"1","article-title":"Inductive representation learning on large graphs","volume-title":"Proc. NeurIPS","author":"Hamilton"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/3326362"},{"key":"ref4","first-page":"1","article-title":"Graph attention networks","volume-title":"Proc. ICLR","author":"Veli\u010dkovi\u0107"},{"key":"ref5","article-title":"Relational inductive biases, deep learning, and graph networks","author":"Battaglia","year":"2018","journal-title":"arXiv:1806.01261"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219890"},{"key":"ref7","article-title":"Fake news detection on social media using geometric deep learning","author":"Monti","year":"2019","journal-title":"arXiv:1902.06673"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2020.01.021"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-019-0666-6"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btaa598"},{"key":"ref11","article-title":"Open graph benchmark: Datasets for machine learning on graphs","author":"Hu","year":"2020","journal-title":"arXiv:2005.00687"},{"key":"ref12","first-page":"1","article-title":"OGB-LSC: A large-scale challenge for machine learning on graphs","volume-title":"Proc. KDD Cup","author":"Hu"},{"key":"ref13","first-page":"1","article-title":"Large-scale graph representation learning with very deep GNNs and self-supervision","volume-title":"Proc. KDD Cup","author":"Addanki"},{"key":"ref14","first-page":"1","article-title":"How powerful are graph neural networks?","volume-title":"Proc. ICLR","author":"Xu"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00936"},{"key":"ref16","first-page":"13260","article-title":"Principal neighbourhood aggregation for graph nets","volume-title":"Proc. NeurIPS","author":"Corso"},{"key":"ref17","article-title":"Benchmarking graph neural networks","author":"Dwivedi","year":"2020","journal-title":"arXiv:2003.00982"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3011866"},{"key":"ref19","first-page":"1","article-title":"Strategies for pre-training graph neural networks","volume-title":"Proc. ICLR","author":"Hu"},{"key":"ref20","first-page":"1","article-title":"Graph contrastive learning with augmentations","volume-title":"Proc. NeurIPS","author":"You"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403168"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM50108.2020.00031"},{"key":"ref23","first-page":"1","article-title":"SIGN: Scalable inception graph neural networks","volume-title":"Proc. ICML Workshop Graph Represent. Learn. Beyond","author":"Frasca"},{"key":"ref24","first-page":"1","article-title":"Adaptive filters and aggregator fusion for efficient graph convolutions","volume-title":"Proc. MLSys GNNSys Workshop","author":"Tailor"},{"key":"ref25","first-page":"6437","article-title":"Training graph neural networks with 1000 layers","volume-title":"Proc. ICML","author":"Li"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/195"},{"key":"ref27","first-page":"1","article-title":"Probabilistic dual network architecture search on graphs","volume-title":"Proc. AAAI Workshop","author":"Zhao"},{"key":"ref28","article-title":"Learned low precision graph neural networks","author":"Zhao","year":"2020","journal-title":"arXiv:2009.09232"},{"key":"ref29","first-page":"1","article-title":"Degree-Quant: Quantization-aware training for graph neural networks","volume-title":"Proc. ICLR","author":"Tailor"},{"key":"ref30","first-page":"1","article-title":"GraphSAINT: Graph sampling based inductive learning method","volume-title":"Proc. ICLR","author":"Zeng"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1503.02531"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-021-01453-z"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00710"},{"key":"ref34","first-page":"1","article-title":"On the bottleneck of graph neural networks and its practical implications","volume-title":"Proc. ICLR","author":"Alon"},{"key":"ref35","article-title":"Representation learning with contrastive predictive coding","author":"Van Den Oord","year":"2018","journal-title":"arXiv:1807.03748"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00393"},{"key":"ref37","first-page":"1597","article-title":"A simple framework for contrastive learning of visual representations","volume-title":"Proc. ICML","author":"Chen"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr46437.2021.01603"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.170"},{"key":"ref40","first-page":"5","volume-title":"Transformers are Graph Neural Networks","author":"Joshi","year":"2020"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1015"},{"key":"ref42","article-title":"An efficient graph convolutional network technique for the travelling salesman problem","author":"Joshi","year":"2019","journal-title":"arXiv:1906.01227"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/595"},{"key":"ref44","article-title":"FitNets: Hints for thin deep nets","author":"Romero","year":"2014","journal-title":"arXiv:1412.6550"},{"key":"ref45","article-title":"Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer","author":"Zagoruyko","year":"2016","journal-title":"arXiv:1612.03928"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.776"},{"key":"ref47","article-title":"In defense of feature mimicking for knowledge distillation","author":"Wang","year":"2020","journal-title":"arXiv:2011.01424"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00726"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00409"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00145"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403236"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3450068"},{"key":"ref53","article-title":"Graph-less neural networks: Teaching old MLPs new tricks via distillation","author":"Zhang","year":"2021","journal-title":"arXiv:2110.08727"},{"key":"ref54","article-title":"On self-distilling graph neural network","author":"Chen","year":"2020","journal-title":"arXiv:2011.02255"},{"key":"ref55","first-page":"2220","article-title":"Rethinking pooling in graph neural networks","volume-title":"Proc. NeurIPS","author":"Mesquita"},{"key":"ref56","article-title":"Geometric deep learning: Grids, groups, graphs, geodesics, and gauges","author":"Bronstein","year":"2021","journal-title":"arXiv:2104.13478"},{"key":"ref57","first-page":"1","article-title":"PointNet++: Deep hierarchical feature learning on point sets in a metric space","volume-title":"Proc. NIPS","author":"Qi"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00651"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00319"},{"key":"ref60","first-page":"1","article-title":"Point-Voxel CNN for efficient 3D deep learning","volume-title":"Proc. NeurIPS","author":"Liu"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58604-1_41"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/3DV50981.2020.00029"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1039\/C7SC02664A"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1162\/qss_a_00021"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298801"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00167"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-93417-4_38"},{"key":"ref68","article-title":"Fast graph representation learning with PyTorch geometric","author":"Fey","year":"2019","journal-title":"arXiv:1903.02428"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1109\/TETC.2020.3014636"},{"key":"ref70","first-page":"3519","article-title":"Similarity of neural network representations revisited","volume-title":"Proc. ICML","author":"Kornblith"},{"key":"ref71","first-page":"209","article-title":"The detection of disease clustering and a generalized regression approach","volume":"27","author":"Mantel","year":"1967","journal-title":"Cancer Res."},{"key":"ref72","first-page":"1","article-title":"Combining label propagation and simple models out-performs graph neural networks","volume-title":"Proc. ICLR","author":"Huang"},{"key":"ref73","volume-title":"Numerical Ecology","author":"Legendre","year":"2012"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/320"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5962385\/10492491\/09969463.pdf?arnumber=9969463","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,9]],"date-time":"2024-04-09T19:39:07Z","timestamp":1712691547000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9969463\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4]]},"references-count":74,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2022.3223018","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4]]}}}