{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T10:00:15Z","timestamp":1775815215400,"version":"3.50.1"},"reference-count":60,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"5","license":[{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Science and Technology Innovation 2030-New Generation Artificial Intelligence","award":["2018AAA0102100"],"award-info":[{"award-number":["2018AAA0102100"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61976018"],"award-info":[{"award-number":["61976018"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Beijing Natural Science Foundation","award":["7222313"],"award-info":[{"award-number":["7222313"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Artif. Intell."],"published-print":{"date-parts":[[2024,5]]},"DOI":"10.1109\/tai.2023.3316202","type":"journal-article","created":{"date-parts":[[2023,9,18]],"date-time":"2023-09-18T14:08:41Z","timestamp":1695046121000},"page":"2204-2216","source":"Crossref","is-referenced-by-count":9,"title":["Adversarial Graph Disentanglement With Component-Specific Aggregation"],"prefix":"10.1109","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8560-8135","authenticated-orcid":false,"given":"Shuai","family":"Zheng","sequence":"first","affiliation":[{"name":"Institute of Information Science, Beijing Jiaotong University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7315-3276","authenticated-orcid":false,"given":"Zhenfeng","family":"Zhu","sequence":"additional","affiliation":[{"name":"Institute of Information Science, Beijing Jiaotong University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7571-4161","authenticated-orcid":false,"given":"Zhizhe","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Information Science, Beijing Jiaotong University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1289-2758","authenticated-orcid":false,"given":"Jian","family":"Cheng","sequence":"additional","affiliation":[{"name":"National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences and University of Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8581-9554","authenticated-orcid":false,"given":"Yao","family":"Zhao","sequence":"additional","affiliation":[{"name":"Institute of Information Science, Beijing Jiaotong University, Beijing, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939753"},{"key":"ref2","article-title":"Variational graph auto-encoders","author":"Kipf","year":"2016"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-010-0210-x"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741093"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/3159652.3159706"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/2806416.2806512"},{"key":"ref9","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Kipf","year":"2016"},{"key":"ref10","article-title":"Graph attention networks","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Velikovi","year":"2018"},{"key":"ref11","first-page":"1024","article-title":"Inductive representation learning on large graphs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Hamilton","year":"2017"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"ref13","article-title":"How powerful are graph neural networks?","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Xu","year":"2018"},{"key":"ref14","first-page":"14245","article-title":"Are disentangled representations helpful for abstract visual reasoning?","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"van Steenkiste","year":"2019"},{"key":"ref15","first-page":"4114","article-title":"Challenging common assumptions in the unsupervised learning of disentangled representations","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Locatello","year":"2019"},{"key":"ref16","first-page":"4212","article-title":"Disentangled graph convolutional networks","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Ma","year":"2019"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5929"},{"key":"ref18","first-page":"20286","article-title":"Factorizable graph convolutional networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Yang","year":"2020"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-73194-6_1"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2022.3195336"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3511929"},{"key":"ref22","first-page":"620","article-title":"Decoupled self-supervised learning for graphs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Xiao","year":"2022"},{"key":"ref23","first-page":"21872","article-title":"Disentangled contrastive learning on graphs","volume":"34","author":"Li","year":"2021","journal-title":"Proc. Adv. Neural Inf. Process. Syst."},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.5555\/2969033.2969125"},{"key":"ref25","article-title":"MolGAN: An implicit generative model for small molecular graphs","author":"De Cao","year":"2018"},{"key":"ref26","first-page":"610","article-title":"Netgan: Generating graphs via random walks","volume-title":"Proc. 35th Int. Conf. Mach. Learn.","author":"Bojchevski","year":"2018"},{"key":"ref27","article-title":"Adversarial feature learning","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Donahue","year":"2016"},{"key":"ref28","article-title":"Adversarially learned inference","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Dumoulin","year":"2016"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/s11063-022-10977-5"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11872"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401079"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/362"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00725"},{"key":"ref34","article-title":"Deep graph infomax","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Velikovi","year":"2018"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.coling-main.47"},{"key":"ref36","first-page":"6056","article-title":"Robustly disentangled causal mechanisms: Validating deep representations for interventional robustness","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Suter","year":"2019"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.191"},{"key":"ref38","first-page":"1180","article-title":"Unsupervised domain adaptation by backpropagation","volume-title":"Proc. 32th Int. Conf. Mach. Learn.","author":"Ganin","year":"2015"},{"key":"ref39","first-page":"19314","article-title":"Iterative deep graph learning for graph neural networks: Better and robust node embeddings","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Chen","year":"2020"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1214\/154957806000000078"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-19992-4_54"},{"key":"ref42","article-title":"Adam: A method for stochastic optimization","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Kingma","year":"2015"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v29i3.2157"},{"key":"ref44","first-page":"496","article-title":"Link-based classification","volume-title":"Proc. 20th Int. Conf. Mach. Learn.","author":"Lu","year":"2003"},{"key":"ref45","first-page":"1","article-title":"Query-driven active surveying for collective classification","volume-title":"Proc. 10th Int. Workshop Mining Learn. Graph.","volume":"8","author":"Namata","year":"2012"},{"key":"ref46","article-title":"Pitfalls of graph neural network evaluation","author":"Shchur","year":"2018"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.3115\/1073445.1073478"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1145\/1557019.1557109"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1606.09375"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3054830"},{"key":"ref51","article-title":"Predict then propagate: Graph neural networks meet personalized pagerank","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Klicpera","year":"2018"},{"key":"ref52","first-page":"6861","article-title":"Simplifying graph convolutional networks","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Wu","year":"2019"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3025110"},{"key":"ref54","first-page":"912","article-title":"Semi-supervised learning using Gaussian fields and harmonic functions","volume-title":"Proc. 20th Int. Conf. Mach. Learn.","author":"Zhu","year":"2003"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.576"},{"key":"ref56","first-page":"40","article-title":"Revisiting semi-supervised learning with graph embeddings","volume-title":"Proc 33th Int. Conf. Mach. Learn.","author":"Yang","year":"2016"},{"key":"ref57","first-page":"115","article-title":"Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures","volume-title":"Proc. 30th Int. Conf. Mach. Learn.","author":"Bergstra","year":"2013"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1982.1056489"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220078"},{"key":"ref60","article-title":"Adversarial attacks on graph neural networks via Meta learning","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Zgner","year":"2019"}],"container-title":["IEEE Transactions on Artificial Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9078688\/10532210\/10254267.pdf?arnumber=10254267","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T01:08:54Z","timestamp":1755911334000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10254267\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5]]},"references-count":60,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.1109\/tai.2023.3316202","relation":{},"ISSN":["2691-4581"],"issn-type":[{"value":"2691-4581","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5]]}}}