{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T18:42:15Z","timestamp":1770748935346,"version":"3.50.0"},"reference-count":69,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"12","license":[{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"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":["62202172"],"award-info":[{"award-number":["62202172"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Science and Technology Committee General Program","award":["22ZR1419900"],"award-info":[{"award-number":["22ZR1419900"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Knowl. Data Eng."],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1109\/tkde.2024.3436909","type":"journal-article","created":{"date-parts":[[2024,8,2]],"date-time":"2024-08-02T17:38:16Z","timestamp":1722620296000},"page":"8670-8681","source":"Crossref","is-referenced-by-count":4,"title":["Learning Prioritized Node-Wise Message Propagation in Graph Neural Networks"],"prefix":"10.1109","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-1241-7188","authenticated-orcid":false,"given":"Yao","family":"Cheng","sequence":"first","affiliation":[{"name":"East China Normal University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minjie","family":"Chen","sequence":"additional","affiliation":[{"name":"East China Normal University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Caihua","family":"Shan","sequence":"additional","affiliation":[{"name":"Microsoft Research Asia, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-0142-2483","authenticated-orcid":false,"given":"Xiang","family":"Li","sequence":"additional","affiliation":[{"name":"East China Normal University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/1772690.1772756"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btx252"},{"key":"ref3","first-page":"1","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Kipf"},{"key":"ref4","first-page":"6861","article-title":"Simplifying graph convolutional networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Wu"},{"key":"ref5","first-page":"1","article-title":"Graph attention networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Veli\u010dkovi\u0107"},{"key":"ref6","first-page":"5449","article-title":"Representation learning on graphs with jumping knowledge networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Xu"},{"key":"ref7","article-title":"Predict then propagate: Graph neural networks meet personalized pagerank","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Gasteiger"},{"key":"ref8","first-page":"1725","article-title":"Simple and deep graph convolutional networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Chen"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16514"},{"key":"ref10","first-page":"13242","article-title":"Finding global homophily in graph neural networks when meeting heterophily","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Li"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i4.25573"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/1242572.1242600"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17332"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467451"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539387"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411872"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.89.208701"},{"key":"ref18","first-page":"1","article-title":"How powerful are graph neural networks?","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Xu"},{"key":"ref19","first-page":"1","article-title":"Dropedge: Towards deep graph convolutional networks on node classification","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Rong"},{"key":"ref20","first-page":"22092","article-title":"Graph random neural networks for semi-supervised learning on graphs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Feng"},{"key":"ref21","first-page":"1","article-title":"AdaGCN: Adaboosting graph convolutional networks into deep models","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Sun"},{"key":"ref22","first-page":"3950","article-title":"Adaptive universal generalized pagerank graph neural network","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Chien"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM54844.2022.00169"},{"key":"ref24","first-page":"3837","article-title":"Convolutional neural networks on graphs with fast localized spectral filtering","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Defferrard"},{"key":"ref25","first-page":"1024","article-title":"Inductive representation learning on large graphs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Hamilton"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403076"},{"key":"ref27","first-page":"338","article-title":"PairNorm: Tackling oversmoothing in GNNs","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Zhao"},{"key":"ref28","first-page":"1","article-title":"FastGCB: Fast learning with graph convolutional networks via importance sampling","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Chen"},{"key":"ref29","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":"ref30","first-page":"1057","article-title":"Policy gradient methods for reinforcement learning with function approximation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Sutton"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1145\/3447772"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441720"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219890"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/214"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512207"},{"key":"ref36","first-page":"241","volume-title":"Networks\u2013An Introduction","author":"Brede","year":"2012"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467447"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.26599\/TST.2021.9010057"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE51399.2021.00054"},{"key":"ref40","first-page":"24509","article-title":"AutoGEL: An automated graph neural network with explicit link information","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Wang"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE48307.2020.00044"},{"key":"ref42","first-page":"40","article-title":"Revisiting semi-supervised learning with graph embeddings","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Yang"},{"key":"ref43","first-page":"40","article-title":"Geom-GCN: Geometric graph convolutional networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Pei"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1093\/comnet\/cnab014"},{"key":"ref45","first-page":"1","article-title":"Adam: A method for stochastic optimization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Kingma"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1247"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3068344"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583498"},{"key":"ref49","first-page":"161","article-title":"The PageRank citation ranking: Bringing order to the web","volume":"98","author":"Brin","year":"1998","journal-title":"Proc. ASIS"},{"key":"ref50","first-page":"21","article-title":"Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Abu-El-Haija"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i4.20319"},{"key":"ref52","first-page":"10654","article-title":"Universal graph convolutional networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Jin"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i4.20340"},{"key":"ref54","article-title":"Graph neural networks for graphs with heterophily: A survey","author":"Zheng","year":"2022"},{"key":"ref55","article-title":"Is heterophily a real nightmare for graph neural networks to do node classification?","author":"Luan","year":"2021"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467373"},{"key":"ref57","first-page":"4751","article-title":"Diverse message passing for attribute with heterophily","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Yang"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-022-1339-7"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-022-2220-4"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-022-2100-y"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-022-2256-5"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-022-1734-0"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-023-2483-4"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01626"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00138"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58529-7_8"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01385"},{"key":"ref68","first-page":"119","article-title":"Rethinking architecture selection in differentiable NAS","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Wang"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1109\/WACV51458.2022.00358"}],"container-title":["IEEE Transactions on Knowledge and Data Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/69\/10750897\/10620634.pdf?arnumber=10620634","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T00:12:35Z","timestamp":1732666355000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10620634\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12]]},"references-count":69,"journal-issue":{"issue":"12"},"URL":"https:\/\/doi.org\/10.1109\/tkde.2024.3436909","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":[[2024,12]]}}}