{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T01:40:05Z","timestamp":1755913205942,"version":"3.44.0"},"reference-count":39,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"9","license":[{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62076004"],"award-info":[{"award-number":["62076004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Anhui Provincial Key Research and Development Program","award":["2022i01020014"],"award-info":[{"award-number":["2022i01020014"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Artif. Intell."],"published-print":{"date-parts":[[2024,9]]},"DOI":"10.1109\/tai.2024.3386499","type":"journal-article","created":{"date-parts":[[2024,4,10]],"date-time":"2024-04-10T14:33:17Z","timestamp":1712759597000},"page":"4315-4321","source":"Crossref","is-referenced-by-count":3,"title":["Incomplete Graph Learning via Partial Graph Convolutional Network"],"prefix":"10.1109","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9429-1635","authenticated-orcid":false,"given":"Ziyan","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Anhui University, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6238-1596","authenticated-orcid":false,"given":"Bo","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Anhui University, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8375-3590","authenticated-orcid":false,"given":"Jin","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Anhui University, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9008-222X","authenticated-orcid":false,"given":"Jinhui","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1414-3307","authenticated-orcid":false,"given":"Bin","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Anhui University, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"Proc. Int. Conf. Learn. Representations (ICLR)","author":"Kipf","year":"2017"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2021.3076974"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2021.3076021"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3195004"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2022.3194869"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3201243"},{"key":"ref7","article-title":"Graph attention networks","volume-title":"Proc. Int. Conf. Learn. Representations (ICLR)","author":"Velickovi\u0107","year":"2018"},{"key":"ref8","article-title":"How powerful are graph neural networks?","volume-title":"Proc. Int. Conf. Learn. Representations (ICLR)","author":"Xu","year":"2019"},{"article-title":"A survey on the expressive power of graph neural networks","year":"2020","author":"Sato","key":"ref9"},{"key":"ref10","first-page":"1024","article-title":"Inductive representation learning on large graphs","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NIPS)","author":"Hamilton","year":"2017"},{"key":"ref11","first-page":"13272","article-title":"Reliable graph neural networks via robust aggregation","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NIPS)","author":"Geisler","year":"2020"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3183143"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2020.11.016"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3032189"},{"issue":"1","key":"ref16","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref17","first-page":"22092","article-title":"Graph random neural networks for semi-supervised learning on graphs","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NIPS)","volume":"33","author":"Feng","year":"2020"},{"key":"ref18","first-page":"5689","article-title":"Gain: Missing data imputation using generative adversarial nets","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Yoon","year":"2018"},{"key":"ref19","first-page":"2672","article-title":"Generative adversarial networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NIPS)","author":"Goodfellow","year":"2014"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411983"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/565"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-10-6704-4_4"},{"article-title":"Robust graph data learning via latent graph convolutional representation","year":"2019","author":"Jiang","key":"ref23"},{"key":"ref24","first-page":"321","article-title":"Learning with local and global consistency","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NIPS)","author":"Zhou","year":"2003"},{"key":"ref25","first-page":"4805","article-title":"Hierarchical graph representation learning with differentiable pooling","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NIPS)","author":"Ying","year":"2018"},{"key":"ref26","article-title":"Variational graph auto-encoders","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NIPS) Bayesian Deep Learn. Workshop","author":"Kipf","year":"2016"},{"key":"ref27","first-page":"478","article-title":"Unsupervised deep embedding for clustering analysis","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Xie","year":"2016"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v29i3.2157"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/2766462.2767755"},{"key":"ref30","first-page":"22118","article-title":"Open graph benchmark: Datasets for machine learning on graphs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Hu","year":"2020"},{"article-title":"Columbia object image library (COIL-20)","year":"1996","author":"Nene","key":"ref31"},{"key":"ref32","article-title":"GraphSAINT: Graph sampling based inductive learning method","volume-title":"Proc. Int. Conf. Learn. Representations (ICLR)","author":"Zeng","year":"2020"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1023\/A:1009953814988"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01157"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1406.3269"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2019.2892416"},{"key":"ref37","first-page":"11:1","article-title":"On the unreasonable effectiveness of feature propagation in learning on graphs with missing node features","volume-title":"Proc. 1st Learn. Graphs Conf.","volume":"198","author":"Rossi","year":"2022"},{"key":"ref38","article-title":"Auto-encoding variational bayes","volume-title":"Proc. Int. Conf. Learn. Representations (ICLR)","author":"Kingma","year":"2014"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i4.25553"}],"container-title":["IEEE Transactions on Artificial Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9078688\/10673734\/10495099.pdf?arnumber=10495099","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T01:09:29Z","timestamp":1755911369000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10495099\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9]]},"references-count":39,"journal-issue":{"issue":"9"},"URL":"https:\/\/doi.org\/10.1109\/tai.2024.3386499","relation":{},"ISSN":["2691-4581"],"issn-type":[{"type":"electronic","value":"2691-4581"}],"subject":[],"published":{"date-parts":[[2024,9]]}}}