{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T20:57:53Z","timestamp":1770065873844,"version":"3.49.0"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2022,1,5]],"date-time":"2022-01-05T00:00:00Z","timestamp":1641340800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,5]],"date-time":"2022-01-05T00:00:00Z","timestamp":1641340800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61971057"],"award-info":[{"award-number":["61971057"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"MoE-CMCC \u201dArtifical Intelligence\u201d Project","award":["MCM20190701"],"award-info":[{"award-number":["MCM20190701"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2022,4]]},"DOI":"10.1007\/s00521-021-06708-x","type":"journal-article","created":{"date-parts":[[2022,1,5]],"date-time":"2022-01-05T00:10:46Z","timestamp":1641341446000},"page":"5549-5559","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["Large-scale cellular traffic prediction based on graph convolutional networks with transfer learning"],"prefix":"10.1007","volume":"34","author":[{"given":"Xu","family":"Zhou","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4997-698X","authenticated-orcid":false,"given":"Yong","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Zhao","family":"Li","sequence":"additional","affiliation":[]},{"given":"Xing","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Juan","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Zhao","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,5]]},"reference":[{"key":"6708_CR1","doi-asserted-by":"crossref","unstructured":"Zhang C, et al. (2021) Dual Attention-based federated learning for wireless traffic prediction. In: IEEE INFOCOM 2021-IEEE Conference on computer communications. IEEE","DOI":"10.1109\/INFOCOM42981.2021.9488883"},{"key":"6708_CR2","doi-asserted-by":"crossref","unstructured":"Sun F, et al. (2021) Mobile data traffic prediction by exploiting time-evolving user mobility patterns. In: IEEE Transactions on mobile computing","DOI":"10.1109\/TMC.2021.3079117"},{"issue":"3","key":"6708_CR3","doi-asserted-by":"publisher","first-page":"2224","DOI":"10.1109\/COMST.2019.2904897","volume":"21","author":"C Zhang","year":"2019","unstructured":"Zhang C, Patras P, Haddadi H (2019) Deep learning in mobile and wireless networking: a survey. IEEE Commun Surv Tutor 21(3):2224\u20132287","journal-title":"IEEE Commun Surv Tutor"},{"issue":"10","key":"6708_CR4","doi-asserted-by":"publisher","first-page":"1151","DOI":"10.3390\/electronics10101151","volume":"10","author":"C Gij\u00f3n","year":"2021","unstructured":"Gij\u00f3n C et al (2021) Long-term data traffic forecasting for network dimensioning in LTE with short time series. Electronics 10(10):1151","journal-title":"Electronics"},{"key":"6708_CR5","unstructured":"Xu W, Zhou Z, Zheng Y, et al. (2017) Spatio-temporal analysis and prediction of cellular traffic in metropolis. In: 2017 IEEE 25th International conference on network protocols (ICNP). IEEE"},{"issue":"10","key":"6708_CR6","doi-asserted-by":"publisher","first-page":"3992","DOI":"10.1093\/ietcom\/e88-b.10.3992","volume":"88","author":"Y Shu","year":"2005","unstructured":"Shu Y, Yu M, Yang O et al (2005) Wireless traffic modeling and prediction using seasonal ARIMA models[J]. IEICE Trans Commun 88(10):3992\u20133999","journal-title":"IEICE Trans Commun"},{"key":"6708_CR7","doi-asserted-by":"crossref","unstructured":"Wang J, Tang J, Xu Z, et al. (2017) Spatiotemporal modeling and prediction in cellular networks: a big data enabled deep learning approach. In: IEEE INFOCOM 2017-IEEE Conference on computer communications. IEEE, 1-9","DOI":"10.1109\/INFOCOM.2017.8057090"},{"key":"6708_CR8","doi-asserted-by":"crossref","unstructured":"Zhang S, Zhao S, Yuan M, et al. (2017) Traffic prediction based power saving in cellular networks: a machine learning method. In: Proceedings of the 25th ACM SIGSPATIAL international conference on advances in geographic information systems, pp. 1\u201310","DOI":"10.1145\/3139958.3140053"},{"key":"6708_CR9","unstructured":"Li Y, Yu R, Shahabi C, et al. (2017) Diffusion convolutional recurrent neural network: Data-driven traffic forecasting[J]. arXiv preprint arXiv:1707.01926"},{"key":"6708_CR10","doi-asserted-by":"crossref","unstructured":"Yu B, Yin H, Zhu Z (2018) Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting[C]. In: IJCAI","DOI":"10.24963\/ijcai.2018\/505"},{"key":"6708_CR11","doi-asserted-by":"crossref","unstructured":"Guo S, Lin Y, Feng N, et al. (2019) Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In: Proceedings of the AAAI conference on artificial intelligence. 33(01): pp. 922\u2013929","DOI":"10.1609\/aaai.v33i01.3301922"},{"issue":"9","key":"6708_CR12","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1177\/0361198120930010","volume":"2674","author":"T Mallick","year":"2020","unstructured":"Mallick T, Balaprakash P, Rask E et al (2020) Graph-partitioning-based diffusion convolutional recurrent neural network for large-scale traffic forecasting[J]. Transp Res Rec 2674(9):473\u2013488","journal-title":"Transp Res Rec"},{"key":"6708_CR13","doi-asserted-by":"crossref","unstructured":"Nie L, Jiang D, Yu S, et al. (2017) Network traffic prediction based on deep belief network in wireless mesh backbone networks. In: 2017 IEEE Wireless communications and networking conference (WCNC). IEEE, pp. 1-5","DOI":"10.1109\/WCNC.2017.7925498"},{"issue":"8","key":"6708_CR14","doi-asserted-by":"publisher","first-page":"1656","DOI":"10.1109\/LCOMM.2018.2841832","volume":"22","author":"C Zhang","year":"2018","unstructured":"Zhang C, Zhang H, Yuan D et al (2018) Citywide cellular traffic prediction based on densely connected convolutional neural networks[J]. IEEE Commun. Lett. 22(8):1656\u20131659","journal-title":"IEEE Commun. Lett."},{"issue":"6","key":"6708_CR15","doi-asserted-by":"publisher","first-page":"3899","DOI":"10.1109\/TWC.2017.2689772","volume":"16","author":"R Li","year":"2017","unstructured":"Li R, Zhao Z, Zheng J et al (2017) The learning and prediction of application-level traffic data in cellular networks[J]. IEEE Trans. Wirel Commun 16(6):3899\u20133912","journal-title":"IEEE Trans. Wirel Commun"},{"issue":"6","key":"6708_CR16","doi-asserted-by":"publisher","first-page":"1389","DOI":"10.1109\/JSAC.2019.2904363","volume":"37","author":"C Zhang","year":"2019","unstructured":"Zhang C, Zhang H, Qiao J et al (2019) Deep transfer learning for intelligent cellular traffic prediction based on cross-domain big data[J]. IEEE J Sel Areas Commun 37(6):1389\u20131401","journal-title":"IEEE J Sel Areas Commun"},{"issue":"1","key":"6708_CR17","doi-asserted-by":"publisher","first-page":"96","DOI":"10.3141\/1748-12","volume":"1748","author":"C Chen","year":"2001","unstructured":"Chen C et al (2001) Freeway performance measurement system: mining loop detector data. Transp Res Rec 1748(1):96\u2013102","journal-title":"Transp Res Rec"},{"issue":"1","key":"6708_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2015.55","volume":"2","author":"G Barlacchi","year":"2015","unstructured":"Barlacchi G, De Nadai M, Larcher R et al (2015) A multi-source dataset of urban life in the city of Milan and the Province of Trentino[J]. Sci Data 2(1):1\u201315","journal-title":"Sci Data"},{"key":"6708_CR19","unstructured":"He K, et al. (2020) Graph attention spatial-temporal network with collaborative global-local learning for citywide mobile traffic prediction. In: IEEE Transactions on mobile computing"},{"issue":"11","key":"6708_CR20","doi-asserted-by":"publisher","first-page":"2532","DOI":"10.1109\/LCOMM.2020.3012279","volume":"24","author":"N Zhao","year":"2020","unstructured":"Zhao N et al (2020) Spatial-temporal attention-convolution network for citywide cellular traffic prediction. IEEE Commun Lett 24(11):2532\u20132536","journal-title":"IEEE Commun Lett"},{"key":"6708_CR21","doi-asserted-by":"crossref","unstructured":"Lee J, Kim H, Lee J, et al. (2017) graph learning for deep learning on graph-structured data. In: Proceedings of the AAAI conference on artificial intelligence. 31(1)","DOI":"10.1609\/aaai.v31i1.10904"},{"key":"6708_CR22","unstructured":"Mallick T, Balaprakash P, Rask E, et al. (2020) Transfer learning with graph neural networks for short-term highway traffic forecasting[J]. arXiv preprint arXiv:2004.08038"},{"key":"6708_CR23","doi-asserted-by":"crossref","unstructured":"He K, Huang Y, Chen X, et al. (2019) Graph attention spatial-temporal network for deep learning based mobile traffic prediction. In: 2019 IEEE Global communications conference (GLOBECOM). IEEE, pp. 1\u20136","DOI":"10.1109\/GLOBECOM38437.2019.9013136"},{"key":"6708_CR24","unstructured":"Kip FTN, Welling M (2016) Semi-supervised classification with graph convolutional networks[J]"},{"key":"6708_CR25","unstructured":"Kalander M, Zhou M, Zhang C, et al. (2020) Spatio-temporal hybrid graph convolutional network for traffic forecasting in telecommunication networks [J]"},{"key":"6708_CR26","unstructured":"Veli\u010dkovi\u0107 P, Cucurull G, Casanova A, et al. (2017) Graph attention networks[J]. arXiv preprint arXiv:1710.10903"},{"key":"6708_CR27","unstructured":"Narayanan A, Chandramohan M, Venkatesan R, et al. (2017) graph2vec: Learning distributed representations of graphs[J]. arXiv preprint arXiv:1707.05005"},{"key":"6708_CR28","unstructured":"Le Q, Mikolov T (2014) Distributed representations of sentences and documents. In: International conference on machine learning. PMLR, pp. 1188\u20131196"},{"key":"6708_CR29","unstructured":"Mikolov T, Sutskever I, Chen K, et al. (2013) Distributed representations of words and phrases and their compositionality[J]. arXiv preprint arXiv:1310.4546"},{"issue":"9","key":"6708_CR30","first-page":"2539","volume":"12","author":"N Shervashidze","year":"2011","unstructured":"Shervashidze N, Schweitzer P, Van Leeuwen EJ et al (2011) Weisfeiler-Lehman graph kernels[J]. J Mach Learn Res 12(9):2539\u20132561","journal-title":"J Mach Learn Res"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06708-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-06708-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06708-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,21]],"date-time":"2023-01-21T21:24:48Z","timestamp":1674336288000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-06708-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,5]]},"references-count":30,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2022,4]]}},"alternative-id":["6708"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-06708-x","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,5]]},"assertion":[{"value":"20 May 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 October 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 January 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}