{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T14:21:54Z","timestamp":1784643714745,"version":"3.55.0"},"reference-count":51,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021ZD0111902"],"award-info":[{"award-number":["2021ZD0111902"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072015"],"award-info":[{"award-number":["62072015"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U21B2038"],"award-info":[{"award-number":["U21B2038"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U19B2039"],"award-info":[{"award-number":["U19B2039"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004826","name":"Beijing Natural Science Foundation","doi-asserted-by":"publisher","award":["4222021"],"award-info":[{"award-number":["4222021"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Research and Development Program of Beijing Municipal Education Commission","award":["KZ202210005008"],"award-info":[{"award-number":["KZ202210005008"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Comput. Soc. Syst."],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1109\/tcss.2023.3292145","type":"journal-article","created":{"date-parts":[[2023,8,7]],"date-time":"2023-08-07T18:22:47Z","timestamp":1691432567000},"page":"2277-2290","source":"Crossref","is-referenced-by-count":12,"title":["CSAT: Contrastive Sampling-Aggregating Transformer for Community Detection in Attribute-Missing Networks"],"prefix":"10.1109","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7540-530X","authenticated-orcid":false,"given":"Mengran","family":"Li","sequence":"first","affiliation":[{"name":"Department of Information Science, Beijing Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6650-6790","authenticated-orcid":false,"given":"Yong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Information Science, Beijing Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8958-8506","authenticated-orcid":false,"given":"Wei","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Economics and Management, China Agricultural University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiyu","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Information Science, Beijing Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinglin","family":"Piao","sequence":"additional","affiliation":[{"name":"Department of Information Science, Beijing Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3121-1823","authenticated-orcid":false,"given":"Baocai","family":"Yin","sequence":"additional","affiliation":[{"name":"Department of Information Science, Beijing Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.physrep.2009.11.002"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2021.3137396"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3032189"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2022.3166539"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TCNS.2022.3163665"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.3028705"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2019.00070"},{"key":"ref8","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2016","journal-title":"arXiv:1609.02907"},{"key":"ref9","first-page":"1025","article-title":"Inductive representation learning on large graphs","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Hamilton"},{"key":"ref10","first-page":"40","article-title":"Revisiting semi-supervised learning with graph embeddings","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Yang"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741093"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"ref14","article-title":"Graph attention networks","author":"Veli\u010dkovi\u0107","year":"2017","journal-title":"arXiv:1710.10903"},{"key":"ref15","first-page":"3700","article-title":"Geometric matrix completion with recurrent multi-graph neural networks","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Monti"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/17.6.520"},{"key":"ref17","article-title":"Wasserstein graph neural networks for graphs with missing attributes","author":"Chen","year":"2021","journal-title":"arXiv:2102.03450"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3130191"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3104155"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.84.066122"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2892096"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11782"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/s00138-021-01251-0"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2010.11.027"},{"key":"ref25","article-title":"Learning from labeled and unlabeled data with label propagation","author":"Xiaojin","year":"2002"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW.2011.154"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.76.036106"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1088\/1367-2630\/12\/10\/103018"},{"key":"ref29","article-title":"Graph convolutional matrix completion","author":"van den Berg","year":"2017","journal-title":"arXiv:1706.02263"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2019.00140"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2020.06.005"},{"key":"ref32","first-page":"19075","article-title":"Handling missing data with graph representation learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"You"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781139168489"},{"key":"ref34","first-page":"5812","article-title":"Graph contrastive learning with augmentations","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"You"},{"key":"ref35","article-title":"Deep graph contrastive representation learning","author":"Zhu","year":"2020","journal-title":"arXiv:2006.04131"},{"key":"ref36","first-page":"3469","article-title":"Structure-aware transformer for graph representation learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Chen"},{"key":"ref37","article-title":"Transformer for graphs: An overview from architecture perspective","author":"Min","year":"2022","journal-title":"arXiv:2202.08455"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00936"},{"key":"ref39","first-page":"1725","article-title":"Simple and deep graph convolutional networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Chen"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109631"},{"key":"ref41","article-title":"Variational graph auto-encoders","author":"Kipf","year":"2016","journal-title":"arXiv:1611.07308"},{"key":"ref42","article-title":"Pitfalls of graph neural network evaluation","author":"Shchur","year":"2018","journal-title":"arXiv:1811.05868"},{"key":"ref43","article-title":"GraphSAINT: Graph sampling based inductive learning method","author":"Zeng","year":"2019","journal-title":"arXiv:1907.04931"},{"key":"ref44","article-title":"Efficient estimation of word representations in vector space","author":"Mikolov","year":"2013","journal-title":"arXiv:1301.3781"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0800497105"},{"issue":"3","key":"ref46","first-page":"4","article-title":"Deep graph infomax","volume-title":"Proc. Int. Conf. Learn. Represent.","volume":"2","author":"Velickovic"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330941"},{"key":"ref48","article-title":"Attributed random walk as matrix factorization","volume-title":"Proc. Neural Inf. Process. Syst., Graph Represent. Learn. Workshop","author":"Chen"},{"key":"ref49","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014","journal-title":"arXiv:1412.6980"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1088\/1742-5468\/2005\/09\/P09008"},{"key":"ref51","first-page":"7793","article-title":"Beyond homophily in graph neural networks: Current limitations and effective designs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Zhu"}],"container-title":["IEEE Transactions on Computational Social Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6570650\/10488768\/10210121.pdf?arnumber=10210121","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,3]],"date-time":"2024-04-03T17:55:17Z","timestamp":1712166917000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10210121\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4]]},"references-count":51,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tcss.2023.3292145","relation":{},"ISSN":["2329-924X","2373-7476"],"issn-type":[{"value":"2329-924X","type":"electronic"},{"value":"2373-7476","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4]]}}}