{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T10:44:30Z","timestamp":1756464270150,"version":"3.37.3"},"reference-count":47,"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":["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. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1109\/tpami.2024.3393300","type":"journal-article","created":{"date-parts":[[2024,4,24]],"date-time":"2024-04-24T17:27:32Z","timestamp":1713979652000},"page":"7720-7727","source":"Crossref","is-referenced-by-count":2,"title":["Learning Graph Attentions via Replicator Dynamics"],"prefix":"10.1109","volume":"46","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6238-1596","authenticated-orcid":false,"given":"Bo","family":"Jiang","sequence":"first","affiliation":[{"name":"Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, School of Computer Science and Technology, Anhui University, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9429-1635","authenticated-orcid":false,"given":"Ziyan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, School of Computer Science and Technology, Anhui University, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3417-5013","authenticated-orcid":false,"given":"Sheng","family":"Ge","sequence":"additional","affiliation":[{"name":"Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, School of Computer Science and Technology, Anhui University, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1272-1596","authenticated-orcid":false,"given":"Beibei","family":"Wang","sequence":"additional","affiliation":[{"name":"Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, School of Computer Science and Technology, Anhui University, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6117-6745","authenticated-orcid":false,"given":"Xiao","family":"Wang","sequence":"additional","affiliation":[{"name":"Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, School of Computer Science and Technology, Anhui University, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8375-3590","authenticated-orcid":false,"given":"Jin","family":"Tang","sequence":"additional","affiliation":[{"name":"Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, School of Computer Science and Technology, Anhui University, Hefei, China"}]}],"member":"263","reference":[{"article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Kipf","key":"ref1"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01157"},{"article-title":"Combining neural networks with personalized pagerank for classification on graphs","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Johannes Klicpera","key":"ref3"},{"article-title":"Deep graph infomax","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Veli\u010dkovi\u0107","key":"ref4"},{"key":"ref5","first-page":"139","article-title":"Equilibrium aggregation: Encoding sets via optimization","volume-title":"Proc. 38th Conf. Uncertainty Artif. Intell.","author":"Bartunov"},{"article-title":"Graph attention networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Veli\u010dkovi\u0107","key":"ref6"},{"key":"ref7","first-page":"20286","article-title":"Factorizable graph convolutional networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Yang"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3072345"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301387"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3134200"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3111054"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3070599"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313562"},{"article-title":"Adaptive structural fingerprints for graph attention networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Zhang","key":"ref14"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449896"},{"article-title":"How to find your friendly neighborhood: Graph attention design with self-supervision","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Kim","key":"ref16"},{"key":"ref17","first-page":"2641","article-title":"Learning conjoint attentions for graph neural nets","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"He"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3148299"},{"key":"ref19","first-page":"28877","article-title":"Do transformers really perform badly for graph representation?","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Ying"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449953"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01066"},{"key":"ref22","first-page":"23321","article-title":"Adaptive diffusion in graph neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zhao"},{"article-title":"GRAND: Graph neural diffusion with a source term","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Thorpe","key":"ref23"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/425"},{"key":"ref25","first-page":"13354","article-title":"Diffusion improves graph learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Klicpera"},{"key":"ref26","first-page":"1407","article-title":"Grand: Graph neural diffusion","volume-title":"Proc. Symbiosis Deep Learn. Differ. Equ.","author":"Chamberlain"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3080635"},{"key":"ref28","first-page":"865","article-title":"Matching free trees with replicator equations","volume-title":"Proc. Adv. Neural Inf. Process. Syst.: Natural Synthetic","author":"Pelillo"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v29i3.2157"},{"article-title":"Pitfalls of graph neural network evaluation","volume-title":"Proc. Relational Representation Learn. Workshop","author":"Shchur","key":"ref31"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btx252"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783417"},{"key":"ref34","first-page":"249","article-title":"Understanding the difficulty of training deep feedforward neural networks","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Glorot"},{"article-title":"DropEdge: Towards deep graph convolutional networks on node classification","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Rong","key":"ref35"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/3159652.3159706"},{"key":"ref37","first-page":"1025","article-title":"Inductive representation learning on large graphs","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Hamilton"},{"key":"ref38","first-page":"5453","article-title":"Representation learning on graphs with jumping knowledge networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Xu"},{"key":"ref39","first-page":"11984","article-title":"Implicit graph neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Gu"},{"article-title":"How powerful are graph neural networks?","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Xu","key":"ref40"},{"key":"ref41","first-page":"2539","article-title":"Weisfeiler-Lehman graph kernels","volume":"12","author":"Shervashidze","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref42","first-page":"21997","article-title":"DropGNN: Random dropouts increase the expressiveness of graph neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Papp"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/194"},{"article-title":"Variational graph auto-encoders","year":"2016","author":"Kipf","key":"ref44"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9054363"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1086\/jar.33.4.3629752"},{"article-title":"Graph neural networks for graphs with heterophily: A survey","year":"2022","author":"Zheng","key":"ref47"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/10746266\/10508103.pdf?arnumber=10508103","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T00:08:00Z","timestamp":1732666080000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10508103\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12]]},"references-count":47,"journal-issue":{"issue":"12"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2024.3393300","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"type":"print","value":"0162-8828"},{"type":"electronic","value":"2160-9292"},{"type":"electronic","value":"1939-3539"}],"subject":[],"published":{"date-parts":[[2024,12]]}}}