{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T11:37:18Z","timestamp":1780400238452,"version":"3.54.1"},"reference-count":42,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"5","license":[{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"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":["U22B2017"],"award-info":[{"award-number":["U22B2017"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Comput. Soc. Syst."],"published-print":{"date-parts":[[2024,10]]},"DOI":"10.1109\/tcss.2024.3387487","type":"journal-article","created":{"date-parts":[[2024,5,9]],"date-time":"2024-05-09T17:38:19Z","timestamp":1715276299000},"page":"5973-5983","source":"Crossref","is-referenced-by-count":14,"title":["PAMT: A Novel Propagation-Based Approach via Adaptive Similarity Mask for Node Classification"],"prefix":"10.1109","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7588-6713","authenticated-orcid":false,"given":"Jinsong","family":"Chen","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Institute of Artificial Intelligence, and Hopcroft Center on Computing Science, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1269-8875","authenticated-orcid":false,"given":"Boyu","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, and Hopcroft Center on Computing Science, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-0525-9121","authenticated-orcid":false,"given":"Qiuting","family":"He","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, and Hopcroft Center on Computing Science, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7627-4604","authenticated-orcid":false,"given":"Kun","family":"He","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, and Hopcroft Center on Computing Science, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220077"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.06.099"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2021.03.044"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2022.3157746"},{"key":"ref5","first-page":"1025","article-title":"Inductive representation learning on large graphs","volume-title":"Proc. 31st Int. Conf. Neural Inf. Process. Syst.","author":"Hamilton","year":"2017"},{"key":"ref6","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Kipf","year":"2017"},{"key":"ref7","article-title":"Graph attention networks","volume-title":"Proc. 6th Int. Conf. Learn. Representations","author":"Velickovi\u0107","year":"2018"},{"key":"ref8","first-page":"5453","article-title":"Representation learning on graphs with jumping knowledge networks","volume-title":"Proc. 35th Int. Conf. Mach. Learn.","author":"Xu","year":"2018"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449927"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5747"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403076"},{"key":"ref12","article-title":"Predict then propagate: Graph neural networks meet personalized pagerank","volume-title":"Proc. 7th Int. Conf. Learn. Representations","author":"Klicpera","year":"2019"},{"key":"ref13","first-page":"6861","article-title":"Simplifying graph convolutional networks","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Wu","year":"2019"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441735"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403177"},{"key":"ref16","first-page":"10654","article-title":"Universal graph convolutional networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Jin","year":"2021"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"ref18","first-page":"6437","article-title":"Training graph neural networks with 1000 layers","volume-title":"Proc. 38th Int. Conf. Mach. Learn.","volume":"139","author":"Li","year":"2021"},{"key":"ref19","article-title":"DeeperGCN: All you need to train deeper GCNs","author":"Li","year":"2020"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403177"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i4.20340"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i4.20319"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330851"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403049"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/669"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM54844.2022.00028"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2017.10.019"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401063"},{"key":"ref30","article-title":"Learning from labeled and unlabeled data with label propagation","author":"Zhu","year":"2002"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/0893-6080(91)90009-T"},{"key":"ref32","first-page":"478","article-title":"Unsupervised deep embedding for clustering analysis","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Xie","year":"2016"},{"key":"ref33","article-title":"CAGNN: Cluster-aware graph neural networks for unsupervised graph representation learning","author":"Zhu","year":"2020"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1023\/A:1009953814988"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v29i3.2157"},{"key":"ref36","first-page":"1","article-title":"Query-driven active surveying for collective classification","volume-title":"Proc. Int. Workshop Mining Learn. Graphs","volume":"8","author":"Namata","year":"2012"},{"key":"ref37","article-title":"Pitfalls of graph neural network evaluation","author":"Shchur","year":"2018"},{"key":"ref38","article-title":"Fast graph representation learning with PyTorch Geometric","volume-title":"Proc. ICLR Workshop Representation Learn. Graphs Manifolds","author":"Fey","year":"2019"},{"key":"ref39","article-title":"Adam: A method for stochastic optimization","volume-title":"Proc. 3rd Int. Conf. Learn. Representations","author":"Kingma","year":"2015"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.2995748"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2018.12.010"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1145\/3418284"}],"container-title":["IEEE Transactions on Computational Social Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6570650\/10704022\/10526397.pdf?arnumber=10526397","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T19:15:47Z","timestamp":1733339747000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10526397\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10]]},"references-count":42,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.1109\/tcss.2024.3387487","relation":{},"ISSN":["2329-924X","2373-7476"],"issn-type":[{"value":"2329-924X","type":"electronic"},{"value":"2373-7476","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10]]}}}