{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T15:06:34Z","timestamp":1776783994535,"version":"3.51.2"},"reference-count":50,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"7","license":[{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100000923","name":"ARC Future Fellowship","doi-asserted-by":"publisher","award":["FT210100097"],"award-info":[{"award-number":["FT210100097"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]},{"name":"S&T Program of Hebei","award":["21340301D."],"award-info":[{"award-number":["21340301D."]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62172370"],"award-info":[{"award-number":["62172370"]}],"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":[[2024,7]]},"DOI":"10.1109\/tnnls.2022.3216630","type":"journal-article","created":{"date-parts":[[2022,11,10]],"date-time":"2022-11-10T20:30:04Z","timestamp":1668112204000},"page":"8882-8896","source":"Crossref","is-referenced-by-count":17,"title":["Toward Graph Self-Supervised Learning With Contrastive Adjusted Zooming"],"prefix":"10.1109","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3540-8845","authenticated-orcid":false,"given":"Yizhen","family":"Zheng","sequence":"first","affiliation":[{"name":"Department of Data Science and AI, Faculty of IT, Monash University, Clayton, VIC, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6833-4811","authenticated-orcid":false,"given":"Ming","family":"Jin","sequence":"additional","affiliation":[{"name":"Department of Data Science and AI, Faculty of IT, Monash University, Clayton, VIC, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0794-527X","authenticated-orcid":false,"given":"Shirui","family":"Pan","sequence":"additional","affiliation":[{"name":"School of Information and Communication Technology, Griffith University, Gold Coast, QLD, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4651-2821","authenticated-orcid":false,"given":"Yuan-Fang","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Data Science and AI, Faculty of IT, Monash University, Clayton, VIC, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7422-630X","authenticated-orcid":false,"given":"Hao","family":"Peng","sequence":"additional","affiliation":[{"name":"Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1218-2804","authenticated-orcid":false,"given":"Ming","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5056-0351","authenticated-orcid":false,"given":"Zhao","family":"Li","sequence":"additional","affiliation":[{"name":"Alibaba Group, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"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","doi-asserted-by":"publisher","DOI":"10.1109\/jproc.2024.3369017"},{"key":"ref3","first-page":"1","article-title":"Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization","volume-title":"Proc. ICLR","author":"Sun"},{"key":"ref4","first-page":"1","article-title":"SchNet: A continuous-filter convolutional neural network for modeling quantum interactions","volume-title":"Proc. NIPS","author":"Sch\u00fctt"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2021.3076021"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2021.3075223"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3068344"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-75762-5_12"},{"key":"ref9","first-page":"2702","article-title":"Discriminative embeddings of latent variable models for structured data","volume-title":"Proc. ICML","author":"Dai"},{"key":"ref10","first-page":"1","article-title":"Graph attention networks","volume-title":"Proc. ICLR","author":"Veli\u010dkovi\u0107"},{"key":"ref11","first-page":"1","article-title":"Simplifying graph convolutional networks","volume-title":"Proc. ICML","author":"Wu"},{"key":"ref12","first-page":"1","article-title":"Deep graph infomax","volume-title":"Proc. ICLR","author":"Velickovic"},{"key":"ref13","first-page":"1","article-title":"Deep graph contrastive representation learning","volume-title":"Proc. ICML","author":"Zhu"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380112"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM50108.2020.00031"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/tkde.2022.3172903"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1312.6203"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1606.09375"},{"key":"ref20","article-title":"Deep convolutional networks on graph-structured data","author":"Henaff","year":"2015","journal-title":"arXiv:1506.05163"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2012.2235192"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403296"},{"key":"ref24","first-page":"1","article-title":"GraphSAINT: Graph sampling based inductive learning method","volume-title":"Proc. ICLR","author":"Zeng"},{"key":"ref25","first-page":"1","article-title":"Diffusion improves graph learning","volume-title":"Proc. NIPS","author":"Klicpera"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.6052"},{"key":"ref27","first-page":"1","article-title":"Lanczosnet: Multi-scale deep graph convolutional networks","volume-title":"Proc. ICLR","author":"Liao"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1145\/3450316"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3070843"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i05.6508"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2935152"},{"key":"ref32","first-page":"1","article-title":"Variational graph auto-encoders","volume-title":"Proc. NIPS","author":"Kipf"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2019.2932096"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"ref36","first-page":"1","article-title":"Inductive representation learning on large graphs","volume-title":"Proc. NIPS","author":"Hamilton"},{"key":"ref37","first-page":"1","article-title":"Contrastive multi-view representation learning on graphs","volume-title":"Proc. ICML","author":"Hassani"},{"key":"ref38","first-page":"1","article-title":"A simple framework for contrastive learning of visual representations","volume-title":"Proc. ICML","author":"Chen"},{"key":"ref39","first-page":"1","article-title":"Learning deep representations by mutual information estimation and maximization","author":"Hjelm","year":"2019","journal-title":"Proc. ICLR"},{"key":"ref40","first-page":"1","article-title":"Unsupervised learning of visual features by contrasting cluster assignments","volume-title":"Proc. NIPS","author":"Caron"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"ref42","first-page":"1","article-title":"Bootstrap your own latent: A new approach to self-supervised learning","volume-title":"Proc. NIPS","author":"Grill"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/204"},{"key":"ref44","article-title":"Open graph benchmark: Datasets for machine learning on graphs","author":"Hu","year":"2020","journal-title":"arXiv:2005.00687"},{"key":"ref45","article-title":"Representation learning with contrastive predictive coding","author":"van den Oord","year":"2018","journal-title":"arXiv:1807.03748"},{"key":"ref46","first-page":"1","article-title":"Pytorch: An imperative style, high-performance deep learning library","volume-title":"Proc. NIPS","author":"Paszke"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i11.17206"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1021\/ci0342472"},{"issue":"11","key":"ref49","first-page":"1","article-title":"Visualizing data using t-SNE","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3035351"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5962385\/10589508\/09945993.pdf?arnumber=9945993","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T01:45:57Z","timestamp":1733881557000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9945993\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7]]},"references-count":50,"journal-issue":{"issue":"7"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2022.3216630","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7]]}}}