{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T03:48:07Z","timestamp":1775620087523,"version":"3.50.1"},"reference-count":43,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T00:00:00Z","timestamp":1709251200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T00:00:00Z","timestamp":1709251200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T00:00:00Z","timestamp":1709251200000},"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":["62206108"],"award-info":[{"award-number":["62206108"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key scientific and technological R&D Plan of Jilin Province of China","award":["20230201066GX"],"award-info":[{"award-number":["20230201066GX"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61976102"],"award-info":[{"award-number":["61976102"]}],"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":["U19A2065"],"award-info":[{"award-number":["U19A2065"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Maritime AI Research Programme","award":["SMI-2022-MTP-06"],"award-info":[{"award-number":["SMI-2022-MTP-06"]}]},{"name":"AI Singapore OTTC","award":["AISG2-TC-2022-006"],"award-info":[{"award-number":["AISG2-TC-2022-006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Knowl. Data Eng."],"published-print":{"date-parts":[[2024,3]]},"DOI":"10.1109\/tkde.2023.3303212","type":"journal-article","created":{"date-parts":[[2023,8,8]],"date-time":"2023-08-08T17:40:45Z","timestamp":1691516445000},"page":"1099-1112","source":"Crossref","is-referenced-by-count":9,"title":["Enhancing Locally Adaptive Smoothing of Graph Neural Networks Via Laplacian Node Disagreement"],"prefix":"10.1109","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1479-576X","authenticated-orcid":false,"given":"Yu","family":"Wang","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, and the School of Artificial Intelligence, Jilin University, Changchun, Jilin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6077-1873","authenticated-orcid":false,"given":"Liang","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering, Jilin University, Changchun, Jilin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7391-0334","authenticated-orcid":false,"given":"Xiaofeng","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Jilin University, Changchun, Jilin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2697-8093","authenticated-orcid":false,"given":"Yi","family":"Chang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Jilin University, Changchun, Jilin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2211-8176","authenticated-orcid":false,"given":"Ivor W.","family":"Tsang","sequence":"additional","affiliation":[{"name":"Centre for Frontier AI Research and the Institute of High-Performance Computing, Agency for Science, Technology and Research (A*STAR), Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1103\/physreve.69.026113"},{"key":"ref2","article-title":"A fair comparison of graph neural networks for graph classification","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Errica"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v29i3.2157"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.18178\/wcse.2019.06.016"},{"key":"ref5","article-title":"Graph attention networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Velickovic"},{"key":"ref6","first-page":"5171","article-title":"Link prediction based on graph neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zhang"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3080635"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.3014089"},{"key":"ref9","first-page":"1263","article-title":"Neural message passing for quantum chemistry","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Gilmer"},{"key":"ref10","article-title":"Revisiting graph neural networks: All we have is low-pass filters","author":"Nt","year":"2019"},{"key":"ref11","first-page":"6861","article-title":"Simplifying graph convolutional networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Wu"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482225"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"ref14","first-page":"1106","article-title":"Learning steady-states of iterative algorithms over graphs","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Dai"},{"key":"ref15","first-page":"11 984","article-title":"Implicit graph neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Gu"},{"key":"ref16","first-page":"7793","article-title":"Beyond homophily in graph neural networks: Current limitations and effective designs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zhu"},{"key":"ref17","article-title":"Geom-GCN: Geometric graph convolutional networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Pei"},{"key":"ref18","article-title":"Adaptive universal generalized pagerank graph neural network","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Chien"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220078"},{"key":"ref20","article-title":"Adversarial attacks on graph neural networks via meta learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Z\u00fcgner"},{"key":"ref21","first-page":"6837","article-title":"Elastic graph neural networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Liu"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403076"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16514"},{"key":"ref24","article-title":"A note on over-smoothing for graph neural networks","author":"Cai","year":"2020"},{"key":"ref25","article-title":"Graph neural networks exponentially lose expressive power for node classification","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Oono"},{"key":"ref26","first-page":"5453","article-title":"Representation learning on graphs with jumping knowledge networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Xu"},{"key":"ref27","article-title":"Predict then propagate: Graph neural networks meet personalized pagerank","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Gasteiger"},{"key":"ref28","first-page":"1725","article-title":"Simple and deep graph convolutional networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Chen"},{"key":"ref29","first-page":"1024","article-title":"Inductive representation learning on large graphs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Hamilton"},{"key":"ref30","article-title":"The pagerank citation ranking: Bringing order to the web","author":"Page","year":"1999"},{"key":"ref31","article-title":"Measuring and improving the use of graph information in graph neural networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Hou"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/3490478"},{"key":"ref33","first-page":"912","article-title":"Semi-supervised learning using gaussian fields and harmonic functions","volume-title":"Proc. 20th Int. Conf. Mach. Learn.","author":"Zhu"},{"key":"ref34","first-page":"1306","article-title":"Poisson learning: Graph based semi-supervised learning at very low label rates","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Calder"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/1201775.882269"},{"key":"ref36","first-page":"22 118","article-title":"Open graph benchmark: Datasets for machine learning on graphs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Hu"},{"key":"ref37","first-page":"40","article-title":"Revisiting semi-supervised learning with graph embeddings","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Yang"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1145\/1557019.1557108"},{"key":"ref39","article-title":"Adam: A method for stochastic optimization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Kingma"},{"key":"ref40","article-title":"DeepRobust: A PyTorch library for adversarial attacks and defenses","author":"Li","year":"2020"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/669"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371789"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-013-0693-z"}],"container-title":["IEEE Transactions on Knowledge and Data Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/69\/10422839\/10210704.pdf?arnumber=10210704","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,15]],"date-time":"2024-02-15T19:01:34Z","timestamp":1708023694000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10210704\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3]]},"references-count":43,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/tkde.2023.3303212","relation":{},"ISSN":["1041-4347","1558-2191","2326-3865"],"issn-type":[{"value":"1041-4347","type":"print"},{"value":"1558-2191","type":"electronic"},{"value":"2326-3865","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3]]}}}