{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T15:34:34Z","timestamp":1783438474939,"version":"3.54.6"},"reference-count":22,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T00:00:00Z","timestamp":1626566400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T00:00:00Z","timestamp":1626566400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T00:00:00Z","timestamp":1626566400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"publisher","award":["61872215"],"award-info":[{"award-number":["61872215"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,7,18]]},"DOI":"10.1109\/ijcnn52387.2021.9534420","type":"proceedings-article","created":{"date-parts":[[2021,9,21]],"date-time":"2021-09-21T20:40:52Z","timestamp":1632256852000},"page":"1-8","source":"Crossref","is-referenced-by-count":6,"title":["STG-Meta: Spatial-Temporal Graph Meta-Learning for Traffic Forecasting"],"prefix":"10.1109","author":[{"given":"Jiadong","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wang","family":"Pan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qipu","family":"Deng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenwu","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33015668"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2017.10.016"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5438"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5470"},{"key":"ref14","article-title":"Adaptive graph convolutional recurrent network for traffic forecasting","author":"bai","year":"2020","journal-title":"ar Xiv preprint"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.3008774"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330884"},{"key":"ref17","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","author":"finn","year":"0","journal-title":"Int Conference on Machine Learning"},{"key":"ref18","article-title":"Transfer learning with graph neural networks for short-term highway traffic forecasting","author":"mallick","year":"2020","journal-title":"ArXiv Preprint"},{"key":"ref19","first-page":"1","article-title":"NeurIPS-2020-graph-meta-learning-via-local-subgraphs-Paper","author":"huang","year":"2020","journal-title":"NeurIPS"},{"key":"ref4","article-title":"Spatial-temporal fusion graph neural networks for traffic flow forecasting","author":"mengzhang","year":"0","journal-title":"ArXiv Preprint"},{"key":"ref3","article-title":"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting","author":"li","year":"2017","journal-title":"ArXiv Preprint"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313577"},{"key":"ref5","article-title":"Cross-city transfer learning for deep spatio-temporal prediction","author":"wang","year":"2018","journal-title":"ArXiv Preprint"},{"key":"ref8","article-title":"Deep spatio-temporal residual networks for citywide crowd flows prediction","volume":"31","author":"zhang","year":"0","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"ref7","article-title":"Convolutional lstm network: A machine learning approach for precipitation nowcasting","author":"shi","year":"2015","journal-title":"ArXiv Preprint"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/505"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301922"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33015668"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403113"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403358"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403230"}],"event":{"name":"2021 International Joint Conference on Neural Networks (IJCNN)","location":"Shenzhen, China","start":{"date-parts":[[2021,7,18]]},"end":{"date-parts":[[2021,7,22]]}},"container-title":["2021 International Joint Conference on Neural Networks (IJCNN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9533266\/9533267\/09534420.pdf?arnumber=9534420","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T15:45:50Z","timestamp":1652197550000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9534420\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,18]]},"references-count":22,"URL":"https:\/\/doi.org\/10.1109\/ijcnn52387.2021.9534420","relation":{},"subject":[],"published":{"date-parts":[[2021,7,18]]}}}