{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T23:23:07Z","timestamp":1771024987129,"version":"3.50.1"},"reference-count":69,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"6","license":[{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Artif. Intell."],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1109\/tai.2023.3334261","type":"journal-article","created":{"date-parts":[[2023,11,21]],"date-time":"2023-11-21T15:31:13Z","timestamp":1700580673000},"page":"2985-2996","source":"Crossref","is-referenced-by-count":12,"title":["Hierarchical Multiview Top-k Pooling With Deep-Q-Networks"],"prefix":"10.1109","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4135-0838","authenticated-orcid":false,"given":"Zhi-Peng","family":"Li","sequence":"first","affiliation":[{"name":"Eastern Institute of Technology, Ningbo, Zhejiang, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4759-9105","authenticated-orcid":false,"given":"Hai-Long","family":"Su","sequence":"additional","affiliation":[{"name":"Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5628-1858","authenticated-orcid":false,"given":"Yong-","family":"Wu","sequence":"additional","affiliation":[{"name":"Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4232-7736","authenticated-orcid":false,"given":"Qin-Hu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Eastern Institute of Technology, Ningbo, Zhejiang, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6912-718X","authenticated-orcid":false,"given":"Chang-An","family":"Yuan","sequence":"additional","affiliation":[{"name":"Institute of Big Data and Intelligent Computing Research Center, Guangxi Academy of Sciences, Nanning, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9393-351X","authenticated-orcid":false,"given":"Valeriya","family":"Gribova","sequence":"additional","affiliation":[{"name":"Institute of Automation and Control Processes, Far Eastern Branch of the Russian Academy of Sciences, Vladivostok, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8900-8081","authenticated-orcid":false,"given":"Vladimir Fedorovich","family":"Filaretov","sequence":"additional","affiliation":[{"name":"Institute of Automation and Control Processes, Far Eastern Branch of the Russian Academy of Sciences, Vladivostok, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6759-2691","authenticated-orcid":false,"given":"De-Shuang","family":"Huang","sequence":"additional","affiliation":[{"name":"Eastern Institute of Technology, Ningbo, Zhejiang, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TASLP.2014.2339736"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2018.8461870"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2646371"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3077555"},{"key":"ref5","article-title":"Yolov4: Optimal speed and accuracy of object detection","author":"Bochkovskiy","year":"2020"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TBIOM.2022.3184525"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313488"},{"key":"ref10","article-title":"SocialGCN: An efficient graph convolutional network based model for social recommendation","author":"Wu","year":"2018"},{"key":"ref11","article-title":"Three-dimensionally embedded graph convolutional network (3DGCN) for molecule interpretation","author":"Hyeoncheol","year":"2018"},{"key":"ref12","first-page":"6410","article-title":"Graph convolutional policy network for goal-directed molecular graph generation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"You","year":"2018"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5477"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/505"},{"key":"ref15","first-page":"1082","article-title":"Traffic flow prediction via spatial temporal graph neural network","volume-title":"Proc. World Wide Web Conf. (WWW)","author":"Xiaoyang","year":"2020"},{"key":"ref16","first-page":"4800","article-title":"Hierarchical graph representation learning with differentiable pooling","author":"Ying","year":"2018"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557485"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2021.3081010"},{"key":"ref19","first-page":"3098","article-title":"Structure-feature based graph self-adaptive pooling","volume-title":"Proc. World Wide Web Conf. (WWW)","author":"Liang","year":"2020"},{"key":"ref20","first-page":"6661","article-title":"Self-attention graph pooling","volume-title":"Proc. 36th Int. Conf. Mach. Learn. (ICML)","author":"Lee","year":"2019"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449822"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5997"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/tkde.2021.3090664"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TCDS.2021.3100883"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3067441"},{"key":"ref26","article-title":"Playing Atari with deep reinforcement learning","author":"Mnih","year":"2013"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2021.3076021"},{"issue":"5","key":"ref28","first-page":"755","article-title":"A survey on graph convolutional neural network","volume":"43","author":"Xu","year":"2020","journal-title":"Jisuanji Xuebao\/Chin. J. Comput."},{"key":"ref29","first-page":"1","article-title":"Spectral networks and deep locally connected networks on graphs","volume-title":"Proc. 2nd Int. Conf. Learn. Representations (ICLR) Conf. Track.","author":"Bruna","year":"2014"},{"key":"ref30","first-page":"3844","article-title":"Convolutional neural networks on graphs with fast localized spectral filtering","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"29","author":"Defferrard","year":"2016"},{"key":"ref31","first-page":"1","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"Proc. 5th Int. Conf. Learn. Representations, (ICLR) Conf. Track","author":"Kipf","year":"2017"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11691"},{"key":"ref33","article-title":"A generalization of convolutional neural networks to graph-structured data","author":"Hechtlinger","year":"2017"},{"key":"ref34","first-page":"1025","article-title":"Inductive representation learning on large graphs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Hamilton","year":"2017"},{"key":"ref35","first-page":"1","article-title":"Graph attention networks","volume-title":"Proc. 6th Int. Conf. Learn. Represent., (ICLR) Conf. Track,","author":"Veli\u010dkovi\u0107","year":"2018"},{"key":"ref36","first-page":"2053","article-title":"Neural message passing for quantum chemistry","volume-title":"Proc. 34th Int. Conf. Mach. Learn. (ICML)","author":"Gilmer","year":"2017"},{"key":"ref37","article-title":"Gated graph sequence neural networks","author":"Li","year":"2015"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11782"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330982"},{"key":"ref40","article-title":"A survey on multi-view learning","author":"Xu","year":"2013"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2017.02.007"},{"key":"ref42","first-page":"5092","article-title":"COMIC: Multi-view clustering without parameter selection","author":"Peng","year":"2019","journal-title":"Int. Conf. Mach. Learn."},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3197238"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3155499"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1613\/jair.301"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1201\/9781351006620-6"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/tnn.1998.712192"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1016\/0377-2217(89)90348-2"},{"key":"ref49","article-title":"A benchmark data sets for graph kernels","author":"Neumann"},{"key":"ref50","article-title":"TUDataset: A collection of benchmark datasets for learning with graphs","author":"Morris","year":"2020"},{"key":"ref51","article-title":"Benchmarking graph neural networks","author":"Dwivedi","year":"2020"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-007-0103-5"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1016\/S0022-2836(03)00628-4"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bti1007"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1021\/jm00106a046"},{"key":"ref56","first-page":"3756","article-title":"Graph invariant kernels","volume-title":"Proc. Int. Joint Conf. Artif. Intell. (IJCAI)","author":"Orsini","year":"2015,"},{"key":"ref57","first-page":"255","article-title":"Fast neighborhood subgraph pairwise distance kernel","volume-title":"Proc. 27th Int. Conf. Mach. Learn. (ICML)","author":"Costa","year":"2010"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.120"},{"key":"ref59","article-title":"How powerful are graph neural networks?","author":"Xu","year":"2018"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441734"},{"key":"ref61","first-page":"11458","article-title":"Robust graph representation learning via neural sparsification","author":"Zheng","year":"2020","journal-title":"Int. Conf. Mach. Learn."},{"key":"ref62","article-title":"Accurate learning of graph representations with graph multiset pooling","author":"Baek","year":"2021"},{"key":"ref63","first-page":"24017","article-title":"Structural entropy guided graph hierarchical pooling","author":"Wu","year":"2022","journal-title":"Int. Conf. Mach. Learn."},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.2307\/j.ctvcm4g18.8"},{"key":"ref65","article-title":"Fast graph representation learning with PyTorch geometric","author":"Fey","year":"2019"},{"key":"ref66","first-page":"1","article-title":"ADAM: A method for stochastic optimization","volume-title":"Proc. 3rd Int. Conf. Learn. Representations (ICLR) Conf. Track","author":"Kingma","year":"2015"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1038\/nature14236"},{"issue":"3","key":"ref68","first-page":"190","article-title":"Design and convergence analysis of a heuristic reward function for reinforcement learning","volume":"8","author":"Ying Zi","year":"2005","journal-title":"Comput. Sci."},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403177"}],"container-title":["IEEE Transactions on Artificial Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9078688\/10571778\/10324365.pdf?arnumber=10324365","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T01:09:20Z","timestamp":1755911360000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10324365\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6]]},"references-count":69,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.1109\/tai.2023.3334261","relation":{},"ISSN":["2691-4581"],"issn-type":[{"value":"2691-4581","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6]]}}}