{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T09:16:55Z","timestamp":1778059015167,"version":"3.51.4"},"reference-count":66,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"6","license":[{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72495125"],"award-info":[{"award-number":["72495125"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Innovation Program of Hunan Province","award":["#2024RC4008"],"award-info":[{"award-number":["#2024RC4008"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1109\/tnnls.2025.3545111","type":"journal-article","created":{"date-parts":[[2025,3,22]],"date-time":"2025-03-22T00:41:51Z","timestamp":1742604111000},"page":"10479-10490","source":"Crossref","is-referenced-by-count":5,"title":["Unifying Attribute and Structure Preservation for Enhanced Graph Contrastive Learning"],"prefix":"10.1109","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-6590-8304","authenticated-orcid":false,"given":"Jialu","family":"Chen","sequence":"first","affiliation":[{"name":"School of Business Administration, Faculty of Business Administration, Southwestern University of Finance and Economics, Chengdu, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-4133-2754","authenticated-orcid":false,"given":"Rui","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Business Administration, Faculty of Business Administration, Southwestern University of Finance and Economics, Chengdu, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9220-8647","authenticated-orcid":false,"given":"Gang","family":"Kou","sequence":"additional","affiliation":[{"name":"School of Business Administration, Faculty of Business Administration, Southwestern University of Finance and Economics, Chengdu, China"}]}],"member":"263","reference":[{"key":"ref1","first-page":"3835","article-title":"Graph matching networks for learning the similarity of graph structured objects","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Li"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1088\/2632-2153\/abbf9a"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330958"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bty294"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3170559"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3172903"},{"issue":"3","key":"ref7","first-page":"4","article-title":"Deep graph infomax","volume-title":"Proc. ICLR","volume":"2","author":"Velickovic"},{"key":"ref8","article-title":"Deep graph contrastive representation learning","author":"Zhu","year":"2020","journal-title":"arXiv:2006.04131"},{"key":"ref9","first-page":"4116","article-title":"Contrastive multi-view representation learning on graphs","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Hassani"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449802"},{"key":"ref11","first-page":"12121","article-title":"Graph contrastive learning automated","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"You"},{"key":"ref12","article-title":"InfoGraph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization","author":"Sun","year":"2019","journal-title":"arXiv:1908.01000"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380112"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM50108.2020.00031"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441735"},{"key":"ref16","first-page":"10654","article-title":"Universal graph convolutional networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Jin"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403076"},{"key":"ref18","first-page":"13366","article-title":"Diffusion improves graph learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Gasteiger"},{"key":"ref19","article-title":"The information bottleneck method","author":"Tishby","year":"2000","journal-title":"arXiv:0004057"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1126\/science.290.5500.2323"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"ref23","first-page":"2111","article-title":"Network representation learning with rich text information","volume-title":"Proc. 24th Int. Joint Conf. Artif. Intell.","author":"Yang"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.24963\/IJCAI.2018\/467"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2019.00086"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2024.3371592"},{"key":"ref28","first-page":"1","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Kipf"},{"key":"ref29","first-page":"1025","article-title":"Inductive representation learning on large graphs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Hamilton"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2024.3370918"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"ref32","first-page":"1","article-title":"DropEdge: Towards deep graph convolutional networks on node classification","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Rong"},{"key":"ref33","first-page":"21","article-title":"MixHop: Higher-order graph convolutional architectures via sparsified neighborhood mixing","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Abu-El-Haija"},{"key":"ref34","first-page":"1","article-title":"Bootstrapped representation learning on graphs","volume-title":"Proc. ICLR Workshop Geometrical Topological Represent. Learn.","author":"Thakoor"},{"key":"ref35","first-page":"5812","article-title":"Graph contrastive learning with augmentations","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"You"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403168"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i7.20700"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3248871"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/tkde.2024.3423409"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539425"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531937"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512156"},{"key":"ref43","first-page":"30414","article-title":"INFOGCL: Information-aware graph contrastive learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Xu"},{"key":"ref44","first-page":"531","article-title":"Mutual information neural estimation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Belghazi"},{"key":"ref45","first-page":"271","article-title":"F-GAN: Training generative neural samplers using variational divergence minimization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"29","author":"Nowozin"},{"key":"ref46","first-page":"297","article-title":"Noise-contrastive estimation: A new estimation principle for unnormalized statistical models","volume-title":"Proc. 13th Int. Conf. Artif. Intell. Statist.","author":"Gutmann"},{"key":"ref47","first-page":"6827","article-title":"What makes for good views for contrastive learning?","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Tian"},{"issue":"9","key":"ref48","first-page":"1989","article-title":"Fast approximate kNN graph construction for high dimensional data via recursive Lanczos bisection","volume":"10","author":"Chen","year":"2009","journal-title":"J. Mach. Learn. Res."},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2010.9"},{"key":"ref50","first-page":"20437","article-title":"Graph information bottleneck","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Wu"},{"key":"ref51","volume-title":"Elements of Information Theory","author":"Thomas","year":"2006"},{"key":"ref52","first-page":"40","article-title":"Revisiting semi-supervised learning with graph embeddings","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Yang"},{"key":"ref53","article-title":"Pitfalls of graph neural network evaluation","author":"Shchur","year":"2018","journal-title":"arXiv:1811.05868"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599546"},{"key":"ref55","first-page":"1","article-title":"Graph attention networks","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Veli\u010dkovi\u0107"},{"key":"ref56","article-title":"Variational graph auto-encoders","author":"Kipf","year":"2016","journal-title":"arXiv:1611.07308"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/362"},{"key":"ref58","article-title":"MGAE: Masked autoencoders for self-supervised learning on graphs","author":"Tan","year":"2022","journal-title":"arXiv:2201.02534"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539321"},{"key":"ref60","first-page":"1115","article-title":"Adversarial attack on graph structured data","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Dai"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/669"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403049"},{"key":"ref63","article-title":"DeepRobust: A PyTorch library for adversarial attacks and defenses","author":"Li","year":"2020","journal-title":"arXiv:2005.06149"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220078"},{"key":"ref65","first-page":"1","article-title":"Adversarial attacks on graph neural networks via meta learning","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Z\u00fcgner"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5984"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/5962385\/11022714\/10937101.pdf?arnumber=10937101","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,4]],"date-time":"2025-06-04T17:58:02Z","timestamp":1749059882000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10937101\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6]]},"references-count":66,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2025.3545111","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6]]}}}