{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:20:43Z","timestamp":1740108043813,"version":"3.37.3"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"30","license":[{"start":{"date-parts":[[2023,8,8]],"date-time":"2023-08-08T00:00:00Z","timestamp":1691452800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,8,8]],"date-time":"2023-08-08T00:00:00Z","timestamp":1691452800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Starry Night Science Fund of Zhejiang University Shanghai Institute for Advanced Study","award":["SN-ZJU-SIAS-001"],"award-info":[{"award-number":["SN-ZJU-SIAS-001"]}]},{"DOI":"10.13039\/100014718","name":"Innovative Research Group Project of the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1866602"],"award-info":[{"award-number":["U1866602"]}],"id":[{"id":"10.13039\/100014718","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s00521-023-08900-7","type":"journal-article","created":{"date-parts":[[2023,8,8]],"date-time":"2023-08-08T18:01:51Z","timestamp":1691517711000},"page":"22359-22374","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Online cross-layer knowledge distillation on graph neural networks with deep supervision"],"prefix":"10.1007","volume":"35","author":[{"given":"Jiongyu","family":"Guo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Defang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Can","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,8,8]]},"reference":[{"key":"8900_CR1","unstructured":"Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: International conference on learning representations"},{"key":"8900_CR2","unstructured":"Velickovic P, Cucurull G, Casanova A, Romero A, Li\u00f2 P, Bengio Y (2018) Graph attention networks. In: International conference on learning representations"},{"key":"8900_CR3","unstructured":"Hamilton WL, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. In: Advances in neural information processing systems, pp 1024\u20131034"},{"key":"8900_CR4","unstructured":"Kipf TN, Welling M (2016) Variational graph auto-encoders. In: NeurIPS workshop on Bayesian deep learning"},{"key":"8900_CR5","doi-asserted-by":"crossref","unstructured":"Peng Z, Huang W, Luo M, Zheng Q, Rong Y, Xu T, Huang J (2020) Graph representation learning via graphical mutual information maximization. In: The web conference, pp 259\u2013270","DOI":"10.1145\/3366423.3380112"},{"key":"8900_CR6","doi-asserted-by":"crossref","unstructured":"Wang C, Pan S, Hu R, Long G, Jiang J, Zhang C (2019) Attributed graph clustering: a deep attentional embedding approach. In: Proceedings of international joint conference on artificial intelligence, pp 3670\u20133676","DOI":"10.24963\/ijcai.2019\/509"},{"key":"8900_CR7","doi-asserted-by":"crossref","unstructured":"Bucilua C, Caruana R, Niculescu-Mizil A (2006) Model compression. In: Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining, pp 535\u2013541","DOI":"10.1145\/1150402.1150464"},{"key":"8900_CR8","unstructured":"Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network"},{"key":"8900_CR9","unstructured":"Romero A, Ballas N, Kahou SE, Chassang A, Gatta C, Bengio Y (2015) Fitnets: hints for thin deep nets. In: International conference on learning representations"},{"key":"8900_CR10","unstructured":"Zagoruyko S, Komodakis N (2017) Paying more attention to attention: improving the performance of convolutional neural networks via attention transfer. In: International conference on learning representations"},{"key":"8900_CR11","doi-asserted-by":"crossref","unstructured":"Chen D, Mei J, Zhang Y, Wang C, Wang Z, Feng Y, Chen C (2021) Cross-layer distillation with semantic calibration. In: Proceedings of the AAAI conference on artificial intelligence, pp 7028\u20137036","DOI":"10.1609\/aaai.v35i8.16865"},{"key":"8900_CR12","doi-asserted-by":"crossref","unstructured":"Zhang H, Chen D, Wang C (2022) Confidence-aware multi-teacher knowledge distillation. In: International conference on acoustics, speech and signal processing","DOI":"10.1109\/ICASSP43922.2022.9747534"},{"key":"8900_CR13","doi-asserted-by":"crossref","unstructured":"Chen D, Mei J-P, Zhang H, Wang C, Feng Y, Chen C (2022) Knowledge distillation with the reused teacher classifier. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 11933\u201311942","DOI":"10.1109\/CVPR52688.2022.01163"},{"key":"8900_CR14","doi-asserted-by":"crossref","unstructured":"Yang Y, Qiu J, Song M, Tao D, Wang X (2020) Distilling knowledge from graph convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7072\u20137081","DOI":"10.1109\/CVPR42600.2020.00710"},{"key":"8900_CR15","doi-asserted-by":"crossref","unstructured":"He H, Wang J, Zhang Z, Wu F (2022) Compressing deep graph neural networks via adversarial knowledge distillation. In: Proceedings of the 28th ACM SIGKDD international conference on knowledge discovery and data mining","DOI":"10.1145\/3534678.3539315"},{"key":"8900_CR16","doi-asserted-by":"crossref","unstructured":"Joshi CK, Liu F, Xun X, Lin J, Foo C-S (2021) On representation knowledge distillation for graph neural networks. arXiv preprint arXiv:2111.04964","DOI":"10.1109\/TNNLS.2022.3223018"},{"key":"8900_CR17","doi-asserted-by":"crossref","unstructured":"Yang C, Liu J, Shi C (2021) Extract the knowledge of graph neural networks and go beyond it: an effective knowledge distillation framework. In: The web conference, pp 1227\u20131237","DOI":"10.1145\/3442381.3450068"},{"key":"8900_CR18","doi-asserted-by":"crossref","unstructured":"Deng X, Zhang Z (2021) Graph-free knowledge distillation for graph neural networks. In: Proceedings of the international joint conference on artificial intelligence, pp 2321\u20132327","DOI":"10.24963\/ijcai.2021\/320"},{"key":"8900_CR19","doi-asserted-by":"crossref","unstructured":"Li Q, Han Z, Wu X-M (2018) Deeper insights into graph convolutional networks for semi-supervised learning. In: Proceedings of the AAAI conference on artificial intelligence, pp 3538\u20133545","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"8900_CR20","doi-asserted-by":"crossref","unstructured":"Liu M, Gao H, Ji S (2020) Towards deeper graph neural networks. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery and data mining, pp 338\u2013348","DOI":"10.1145\/3394486.3403076"},{"key":"8900_CR21","doi-asserted-by":"crossref","unstructured":"Chen D, Lin Y, Li W, Li P, Zhou J, Sun X (2020) Measuring and relieving the over-smoothing problem for graph neural networks from the topological view. In: Proceedings of the AAAI conference on artificial intelligence, pp 3438\u20133445","DOI":"10.1609\/aaai.v34i04.5747"},{"key":"8900_CR22","doi-asserted-by":"crossref","unstructured":"Zhang Y, Xiang T, Hospedales TM, Lu H (2018) Deep mutual learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4320\u20134328","DOI":"10.1109\/CVPR.2018.00454"},{"key":"8900_CR23","unstructured":"Anil R, Pereyra G, Passos A, Orm\u00e1ndi R, Dahl GE, Hinton GE (2018) Large scale distributed neural network training through online distillation. In: International conference on learning representations"},{"key":"8900_CR24","doi-asserted-by":"crossref","unstructured":"Chen D, Mei JP, Wang C, Feng Y, Chen C (2020) Online knowledge distillation with diverse peers. In: Proceedings of the AAAI conference on artificial intelligence, pp 3430\u20133437","DOI":"10.1609\/aaai.v34i04.5746"},{"key":"8900_CR25","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed SE, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"8900_CR26","unstructured":"Lee CY, Xie S, Gallagher P, Zhang Z, Tu Z (2015) Deeply-supervised nets. In: Artificial intelligence and statistics, pp 562\u2013570"},{"key":"8900_CR27","doi-asserted-by":"crossref","unstructured":"Guo J, Chen D, Wang C (2022) Alignahead: online cross-layer knowledge extraction on graph neural networks. arXiv preprint arXiv:2205.02468","DOI":"10.1109\/IJCNN55064.2022.9892159"},{"key":"8900_CR28","doi-asserted-by":"crossref","unstructured":"Lin S, Xie H, Wang B, Yu K, Chang X, Liang X, Wang G (2022) Knowledge distillation via the target-aware transformer. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 10905\u201310914","DOI":"10.1109\/CVPR52688.2022.01064"},{"key":"8900_CR29","doi-asserted-by":"crossref","unstructured":"Zhao B, Cui Q, Song R, Qiu Y, Liang J (2022) Decoupled knowledge distillation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 11943\u201311952","DOI":"10.1109\/CVPR52688.2022.01165"},{"key":"8900_CR30","doi-asserted-by":"crossref","unstructured":"Klicpera J, Bojchevski A, G\u00fcnnemann S (2019) Predict then propagate: graph neural networks meet personalized pagerank. In: International conference on learning representations","DOI":"10.1145\/3394486.3403296"},{"key":"8900_CR31","unstructured":"Wu F, Jr AHS, Zhang T, Fifty C, Yu T, Weinberger KQ (2019) Simplifying graph convolutional networks. In: International conference on machine learning, pp 6861\u20136871"},{"key":"8900_CR32","unstructured":"Chen M, Wei Z, Huang Z, Ding B, Li Y (2020) Simple and deep graph convolutional networks. In: International conference on machine learning, pp 1725\u20131735"},{"key":"8900_CR33","doi-asserted-by":"crossref","unstructured":"Shi W, Rajkumar RR (2020) Point-gnn: graph neural network for 3d object detection in a point cloud. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1708\u20131716","DOI":"10.1109\/CVPR42600.2020.00178"},{"issue":"5","key":"8900_CR34","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1093\/bib\/bbab051","volume":"22","author":"ZA Shen","year":"2021","unstructured":"Shen ZA, Luo T, Zhou Y-K, Yu H, Du P-F (2021) NPI-GNN: predicting NCRNA\u2013protein interactions with deep graph neural networks. Brief Bioinform 22(5):51","journal-title":"Brief Bioinform"},{"key":"8900_CR35","unstructured":"Rong Y, bing Huang W, Xu T, Huang J (2019) Dropedge: towards deep graph convolutional networks on node classification. In: International conference on learning representations"},{"key":"8900_CR36","unstructured":"Li G, Xiong C, Thabet A, Ghanem B (2020) Deepergcn: all you need to train deeper gcns. arXiv preprint arXiv:2006.07739"},{"key":"8900_CR37","doi-asserted-by":"crossref","unstructured":"Huang C, Zhou H, Za\u00efane OR, Mou L, Li L (2022) Non-autoregressive translation with layer-wise prediction and deep supervision. In: Proceedings of the AAAI conference on artificial intelligence, pp 10776\u201310784","DOI":"10.1609\/aaai.v36i10.21323"},{"key":"8900_CR38","doi-asserted-by":"crossref","unstructured":"Mei Y, Guo X, Sun D, Pan G, Zhang J (2021) Deep supervised image retargeting. In: Proceedings of the IEEE international conference on multimedia and expo, pp 1\u20136","DOI":"10.1109\/ICME51207.2021.9428129"},{"key":"8900_CR39","doi-asserted-by":"crossref","unstructured":"Zhang L, Song J, Gao A, Chen J, Bao C, Ma K (2019) Be your own teacher: improve the performance of convolutional neural networks via self distillation. In: International conference on computer vision, pp 3712\u20133721","DOI":"10.1109\/ICCV.2019.00381"},{"key":"8900_CR40","unstructured":"Elinas P, Bonilla EV (2022) Addressing over-smoothing in graph neural networks via deep supervision. arXiv preprint arXiv:2202.12508"},{"key":"8900_CR41","doi-asserted-by":"crossref","unstructured":"Jiao X, Yin Y, Shang L, Jiang X, Chen X, Li L, Wang F, Liu Q (2019) Tinybert: distilling bert for natural language understanding. arXiv preprint arXiv:1909.10351","DOI":"10.18653\/v1\/2020.findings-emnlp.372"},{"key":"8900_CR42","doi-asserted-by":"crossref","unstructured":"Yang C, Guo Y, Xu YL, Shi C, Liu J, Wang C, Li X, Guo N, Yin H (2023) Learning to distill graph neural networks. In: Proceedings of the sixteenth ACM international conference on web search and data mining","DOI":"10.1145\/3539597.3570480"},{"key":"8900_CR43","doi-asserted-by":"crossref","unstructured":"Ahn S, Hu SX, Damianou AC, Lawrence ND, Dai Z (2019) Variational information distillation for knowledge transfer. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 9163\u20139171","DOI":"10.1109\/CVPR.2019.00938"},{"key":"8900_CR44","doi-asserted-by":"crossref","unstructured":"Yue K, Deng J, Zhou F (2020) Matching guided distillation. In: European conference on computer vision, pp 312\u2013328","DOI":"10.1007\/978-3-030-58555-6_19"},{"issue":"14","key":"8900_CR45","doi-asserted-by":"publisher","first-page":"i190","DOI":"10.1093\/bioinformatics\/btx252","volume":"33","author":"M Zitnik","year":"2017","unstructured":"Zitnik M, Leskovec J (2017) Predicting multicellular function through multi-layer tissue networks. Bioinformatics 33(14):i190\u2013i198","journal-title":"Bioinformatics"},{"key":"8900_CR46","unstructured":"Bojchevski A, G\u00fcnnemann S (2018) Deep gaussian embedding of graphs: unsupervised inductive learning via ranking. In: International conference on learning representations"},{"key":"8900_CR47","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1023\/A:1009953814988","volume":"3","author":"A McCallum","year":"2004","unstructured":"McCallum A, Nigam K, Rennie JDM, Seymore K (2004) Automating the construction of internet portals with machine learning. Inform Retr 3:127\u2013163","journal-title":"Inform Retr"},{"key":"8900_CR48","first-page":"93","volume":"29","author":"P Sen","year":"2008","unstructured":"Sen P, Namata G, Bilgic M, Getoor L, Gallagher B, Eliassi-Rad T (2008) Collective classification in network data. AI Mag 29:93\u2013106","journal-title":"AI Mag"},{"key":"8900_CR49","unstructured":"Namata G, London B, Getoor L, Huang B, Edu U (2012) Query-driven active surveying for collective classification. In: 10th International workshop on mining and learning with graphs, vol\u00a08, p\u00a01"},{"key":"8900_CR50","doi-asserted-by":"crossref","unstructured":"Yang C, An Z, Cai L, Xu Y (2021) Hierarchical self-supervised augmented knowledge distillation. In: Proceedings of the international joint conference on artificial intelligence, pp 1217\u20131223","DOI":"10.24963\/ijcai.2021\/168"},{"key":"8900_CR51","doi-asserted-by":"crossref","unstructured":"Xu G, Liu Z, Li X, Loy CC (2020) Knowledge distillation meets self-supervision. In: European conference on computer vision, pp 588\u2013604","DOI":"10.1007\/978-3-030-58545-7_34"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08900-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-08900-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08900-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T15:05:15Z","timestamp":1694876715000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-08900-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,8]]},"references-count":51,"journal-issue":{"issue":"30","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["8900"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-08900-7","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2023,8,8]]},"assertion":[{"value":"8 November 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 July 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 August 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}