乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T11:15:14Z","timestamp":1726485314148},"reference-count":55,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"10","license":[{"start":{"date-parts":[[2023,10,1]],"date-time":"2023-10-01T00:00:00Z","timestamp":1696118400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,10,1]],"date-time":"2023-10-01T00:00:00Z","timestamp":1696118400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,10,1]],"date-time":"2023-10-01T00:00:00Z","timestamp":1696118400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"National Key R&D Program of China","award":["2019YFB2101801"]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62176221","62276215"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Knowl. Data Eng."],"published-print":{"date-parts":[[2023,10,1]]},"DOI":"10.1109\/tkde.2023.3269771","type":"journal-article","created":{"date-parts":[[2023,4,25]],"date-time":"2023-04-25T18:43:23Z","timestamp":1682448203000},"page":"9973-9984","source":"Crossref","is-referenced-by-count":15,"title":["Spatio-Temporal Dynamic Graph Relation Learning for Urban Metro Flow Prediction"],"prefix":"10.1109","volume":"35","author":[{"ORCID":"http:\/\/orcid.org\/0000-0003-2218-6662","authenticated-orcid":false,"given":"Peng","family":"Xie","sequence":"first","affiliation":[{"name":"School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, P.R. China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-6098-111X","authenticated-orcid":false,"given":"Minbo","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, P.R. China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-7780-104X","authenticated-orcid":false,"given":"Tianrui","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, P.R. China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-9136-7737","authenticated-orcid":false,"given":"Shenggong","family":"Ji","sequence":"additional","affiliation":[{"name":"Tencent Inc., Shenzhen, Guangdong, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-8035-405X","authenticated-orcid":false,"given":"Shengdong","family":"Du","sequence":"additional","affiliation":[{"name":"School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, P.R. China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-3550-3495","authenticated-orcid":false,"given":"Zeng","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, P.R. China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-5947-1374","authenticated-orcid":false,"given":"Junbo","family":"Zhang","sequence":"additional","affiliation":[{"name":"JD iCity, JD Technology, Beijing, China"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v30i1.10011"},{"key":"ref12","article-title":"Physical-virtual collaboration graph network for station-level metro ridership prediction","author":"chen","year":"2020"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10735"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/3267305.3274163"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5477"},{"key":"ref52","article-title":"Spatial-temporal transformer networks for traffic flow forecasting","author":"xu","year":"2020"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2020.3036057"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301922"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411874"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403118"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2020.01.002"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/2996913.2997016"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.3141\/2215-09"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.3141\/1644-14"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/264"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/W14-4012"},{"key":"ref46","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014"},{"key":"ref45","first-page":"8024","article-title":"PyTorch: An imperative style, high-performance deep learning library","author":"paszke","year":"2019","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1023\/B:STCO.0000035301.49549.88"},{"key":"ref47","first-page":"82","article-title":"A summary of traffic flow forecasting methods","volume":"3","author":"liu","year":"2004","journal-title":"J Highway Transp Res Develop"},{"key":"ref42","first-page":"17 804","article-title":"Adaptive graph convolutional recurrent network for traffic forecasting","author":"bai","year":"2020","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2935152"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref43","first-page":"5998","article-title":"Attention is all you need","author":"vaswani","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN48605.2020.9207049"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2021.3065404"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2018.2867042"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2018.2879497"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1049\/iet-its.2019.0442"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2020.3000761"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2019.01.027"},{"key":"ref40","article-title":"A comprehensive survey on trustworthy graph neural networks: Privacy, robustness, fairness, and explainability","author":"dai","year":"2022"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2020.3043250"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.3001195"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-021-02587-w"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2020.107484"},{"key":"ref31","article-title":"Graph neural network for traffic forecasting: A survey","author":"jiang","year":"2021"},{"key":"ref30","first-page":"1","article-title":"Semi-supervised classification with graph convolutional networks","author":"kipf","year":"2017","journal-title":"Proc 5th Int Conf Learn Representations"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/505"},{"key":"ref32","first-page":"1","article-title":"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting","author":"li","year":"2018","journal-title":"Proc 6th Int Conf Learn Representations"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5819"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.2985952"},{"key":"ref39","article-title":"A survey on graph structure learning: Progress and opportunities","author":"zhu","year":"2021"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2018.03.001"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.26599\/TST.2018.9010033"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2891537"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2845863"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2013.2247040"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1049\/iet-its.2016.0208"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2015.08.017"},{"key":"ref28","first-page":"1","article-title":"Spectral networks and deep locally connected networks on graphs","author":"bruna","year":"2014","journal-title":"Proc 2nd Int Conf Learn Representations"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33015668"},{"key":"ref29","first-page":"3837","article-title":"Convolutional neural networks on graphs with fast localized spectral filtering","author":"defferrard","year":"2016","journal-title":"Proc Adv Neural Inf Process Syst"}],"container-title":["IEEE Transactions on Knowledge and Data Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/69\/10251471\/10107812.pdf?arnumber=10107812","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,26]],"date-time":"2023-10-26T18:08:23Z","timestamp":1698343703000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10107812\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,1]]},"references-count":55,"journal-issue":{"issue":"10"},"URL":"https:\/\/doi.org\/10.1109\/tkde.2023.3269771","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":[[2023,10,1]]}}}