{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T04:14:28Z","timestamp":1777868068377,"version":"3.51.4"},"publisher-location":"Cham","reference-count":47,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032234551","type":"print"},{"value":"9783032234568","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-23456-8_27","type":"book-chapter","created":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T11:24:14Z","timestamp":1777548254000},"page":"500-523","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["CCGCRN: Cluster and\u00a0Completion Graph Convolution Recurrent Network for\u00a0Incomplete Traffic Flow Forecasting"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6964-0995","authenticated-orcid":false,"given":"Ruotian","family":"Xie","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7363-881X","authenticated-orcid":false,"given":"Jigang","family":"Wen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5821-9361","authenticated-orcid":false,"given":"Caiping","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8288-5524","authenticated-orcid":false,"given":"Yani","family":"Jin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2163-2723","authenticated-orcid":false,"given":"Kun","family":"Xie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2572-8041","authenticated-orcid":false,"given":"Shiming","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1381-4364","authenticated-orcid":false,"given":"Kuanching","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,24]]},"reference":[{"key":"27_CR1","doi-asserted-by":"crossref","unstructured":"Alonso, M.N., Batres-Estrada, G., Moulin, A.: Deep learning in finance: Prediction of stock returns with long short-term memory networks. Big data and machine learning in quantitative investment 1, 251\u2013277 (2018)","DOI":"10.1002\/9781119522225.ch13"},{"key":"27_CR2","first-page":"17804","volume":"33","author":"L Bai","year":"2020","unstructured":"Bai, L., Yao, L., Li, C., Wang, X., Wang, C.: Adaptive graph convolutional recurrent network for traffic forecasting. Adv. Neural. Inf. Process. Syst. 33, 17804\u201317815 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"27_CR3","doi-asserted-by":"crossref","unstructured":"Box, G.E.P., Pierce, D.A.: Distribution of residual autocorrelations in autoregressive-integrated moving average time series models. J. Am. Stat. Assoc. 65(332), 1509\u20131526 (1970)","DOI":"10.1080\/01621459.1970.10481180"},{"issue":"17","key":"27_CR4","doi-asserted-by":"publisher","first-page":"4597","DOI":"10.1073\/pnas.0900518107","volume":"113","author":"J Buizer","year":"2016","unstructured":"Buizer, J., Jacobs, K., Cash, D.: Making short-term climate forecasts useful: linking science and action. Proc. Natl. Acad. Sci. 113(17), 4597\u20134602 (2016)","journal-title":"Proc. Natl. Acad. Sci."},{"key":"27_CR5","doi-asserted-by":"crossref","unstructured":"Cai, J.-F., Cand\u00e8s, E.J., Shen, Z.: A singular value thresholding algorithm for matrix completion. SIAM J. Optim. 20(4), 1956\u20131982 (2010)","DOI":"10.1137\/080738970"},{"issue":"10","key":"27_CR6","doi-asserted-by":"publisher","first-page":"17201","DOI":"10.1109\/TITS.2022.3171451","volume":"23","author":"L Chen","year":"2022","unstructured":"Chen, L., Shao, W., Lv, M., Chen, W., Zhang, Y., Yang, C.: Aargnn: an attentive attributed recurrent graph neural network for traffic flow prediction considering multiple dynamic factors. IEEE Trans. Intell. Transp. Syst. 23(10), 17201\u201317211 (2022)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"27_CR7","first-page":"3529","volume":"34","author":"W Chen","year":"2020","unstructured":"Chen, W., Ling Chen, Yu., Xie, W.C., Gao, Y., Feng, X.: Multi-range attentive bicomponent graph convolutional network for traffic forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence 34, 3529\u20133536 (2020)","journal-title":"In: Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"27_CR8","doi-asserted-by":"crossref","unstructured":"Deb, R., Wee-Chung, A.: Liew: missing value imputation for the analysis of incomplete traffic accident data. Inf. Sci. 339, 274\u2013289 (2016)","DOI":"10.1016\/j.ins.2016.01.018"},{"key":"27_CR9","unstructured":"Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. Advances in neural information processing systems, 29 (2016)"},{"key":"27_CR10","unstructured":"Diao, C., Zhang, D., et\u00a0al.: A novel spatial-temporal multi-scale alignment graph neural network security model for vehicles prediction. IEEE Trans. Intell. Transp. Syst"},{"key":"27_CR11","doi-asserted-by":"crossref","unstructured":"Duan, W., He, X., Zhou, Z., Thiele, L., Rao, H.: Localised adaptive spatial-temporal graph neural network. In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD \u201923, pp. 448\u2013458 (2023)","DOI":"10.1145\/3580305.3599418"},{"key":"27_CR12","doi-asserted-by":"crossref","unstructured":"Fang, Z., Long, Q., Song, G., Xie, K.: Spatial-temporal graph ode networks for traffic flow forecasting. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 364\u2013373 (2021)","DOI":"10.1145\/3447548.3467430"},{"issue":"6","key":"27_CR13","doi-asserted-by":"publisher","first-page":"3599","DOI":"10.1109\/TCYB.2022.3159661","volume":"53","author":"C Gao","year":"2022","unstructured":"Gao, C., Zhu, J., Zhang, F., Wang, Z., Li, X.: A novel representation learning for dynamic graphs based on graph convolutional networks. IEEE Trans. Cybern. 53(6), 3599\u20133612 (2022)","journal-title":"IEEE Trans. Cybern."},{"key":"27_CR14","doi-asserted-by":"crossref","unstructured":"Gao, K., et al.: Incorporating intra-flow dependencies and inter-flow correlations for traffic matrix prediction. In: 2020 IEEE\/ACM 28th International Symposium on Quality of Service (IWQoS), pp. 1\u201310 (2020)","DOI":"10.1109\/IWQoS49365.2020.9213008"},{"key":"27_CR15","doi-asserted-by":"crossref","unstructured":"Gu, Y., Yan, D., Yan, S., Jiang, Z.: Price forecast with high-frequency finance data: An autoregressive recurrent neural network model with technical indicators. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 2485\u20132492 (2020)","DOI":"10.1145\/3340531.3412738"},{"key":"27_CR16","doi-asserted-by":"crossref","unstructured":"Guo, K., Yongli, H., Sun, Y., Qian, S., Gao, J., Yin, B.: Hierarchical graph convolution network for traffic forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence 35, pp. 151\u2013159 (2021)","DOI":"10.1609\/aaai.v35i1.16088"},{"key":"27_CR17","doi-asserted-by":"crossref","unstructured":"Guo, S., Lin, Y., Feng, N., Song, C., Wan, H.: Attention-based spatial-temporal graph convolutional networks for traffic flow forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence 33, pp. 922\u2013929 (2019)","DOI":"10.1609\/aaai.v33i01.3301922"},{"key":"27_CR18","doi-asserted-by":"crossref","unstructured":"Han, L., Du, B., Sun, L., Fu, Y., Lv, Y., Xiong, H.: Dynamic and multi-faceted spatio-temporal deep learning for traffic speed forecasting. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 547\u2013555 (2021)","DOI":"10.1145\/3447548.3467275"},{"key":"27_CR19","doi-asserted-by":"crossref","unstructured":"He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.-S.: Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web, pp. 173\u2013182 (2017)","DOI":"10.1145\/3038912.3052569"},{"key":"27_CR20","doi-asserted-by":"crossref","unstructured":"He, Z., Chow, C.-Y., Zhang, J.-D.: Stcnn: a spatio-temporal convolutional neural network for long-term traffic prediction. In: 2019 20th IEEE International Conference on Mobile Data Management (MDM), pp. 226\u2013233. IEEE (2019)","DOI":"10.1109\/MDM.2019.00-53"},{"key":"27_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.sysarc.2022.102775","volume":"133","author":"N Hu","year":"2022","unstructured":"Hu, N., Zhang, D., Xie, K., et al.: Multi-range bidirectional mask graph convolution based gru networks for traffic prediction. J. Syst. Architect. 133, 102775 (2022)","journal-title":"J. Syst. Architect."},{"key":"27_CR22","doi-asserted-by":"crossref","unstructured":"Hu, N., Zhang, D., Xie, K., Liang, W., Li, K.-C., Zomaya, A.Y.: Dynamic multi-scale spatial\u2013temporal graph convolutional network for traffic flow prediction. Future Gener. Comput. Syst. 158, 323\u2013332 (2024)","DOI":"10.1016\/j.future.2024.04.052"},{"issue":"2","key":"27_CR23","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1109\/TPWRS.2003.811010","volume":"18","author":"S-J Huang","year":"2003","unstructured":"Huang, S.-J., Shih, K.-R.: Short-term load forecasting via arma model identification including non-gaussian process considerations. IEEE Trans. Power Syst. 18(2), 673\u2013679 (2003)","journal-title":"IEEE Trans. Power Syst."},{"key":"27_CR24","doi-asserted-by":"crossref","unstructured":"Lai, G., Chang, W.-C., Yang, Y., Liu, H.: Modeling long-and short-term temporal patterns with deep neural networks. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 95\u2013104 (2018)","DOI":"10.1145\/3209978.3210006"},{"issue":"8","key":"27_CR25","doi-asserted-by":"publisher","first-page":"2933","DOI":"10.1109\/TITS.2018.2869768","volume":"20","author":"L Li","year":"2018","unstructured":"Li, L., Zhang, J., Wang, Y., Ran, B.: Missing value imputation for traffic-related time series data based on a multi-view learning method. IEEE Trans. Intell. Transp. Syst. 20(8), 2933\u20132943 (2018)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"27_CR26","doi-asserted-by":"crossref","unstructured":"Li, M., Zhu, Z.: Spatial-temporal fusion graph neural networks for traffic flow forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence 35, pp. 4189\u20134196 (2021)","DOI":"10.1609\/aaai.v35i5.16542"},{"key":"27_CR27","unstructured":"Li, Y., Yu, R., Shahabi, C., Liu, Y.: Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv preprint arXiv:1707.01926 (2017)"},{"key":"27_CR28","unstructured":"Liang, W., Li, Y., et\u00a0al.: Spatial-temporal aware inductive graph neural network for c-its data recovery. IEEE Trans. Intell. Transp. Syst"},{"key":"27_CR29","doi-asserted-by":"crossref","unstructured":"Lin, H., Bai, R., Jia, W., Yang, X., You, Y.: Preserving dynamic attention for long-term spatial-temporal prediction. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 36\u201346 (2020)","DOI":"10.1145\/3394486.3403046"},{"key":"27_CR30","doi-asserted-by":"crossref","unstructured":"Liu, C., Hoi, S.C.H., Zhao, P., Sun, J.: Online arima algorithms for time series prediction. In: Thirtieth AAAI Conference on Artificial Intelligence (2016)","DOI":"10.1609\/aaai.v30i1.10257"},{"issue":"4","key":"27_CR31","doi-asserted-by":"publisher","first-page":"818","DOI":"10.3390\/s17040818","volume":"17","author":"X Ma","year":"2017","unstructured":"Ma, X., Dai, Z., He, Z., Ma, J., Wang, Y., Wang, Y.: Learning traffic as images: a deep convolutional neural network for large-scale transportation network speed prediction. Sensors 17(4), 818 (2017)","journal-title":"Sensors"},{"key":"27_CR32","doi-asserted-by":"crossref","unstructured":"Nyadzi, E., Werners, S.E., Biesbroek, R., Ludwig, F.: Techniques and skills of indigenous weather and seasonal climate forecast in northern ghana. Climate Dev. 13(6), 551\u2013562 (2021)","DOI":"10.1080\/17565529.2020.1831429"},{"issue":"22","key":"27_CR33","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1029\/2018GL080704","volume":"45","author":"S Scher","year":"2018","unstructured":"Scher, S.: Toward data-driven weather and climate forecasting: Approximating a simple general circulation model with deep learning. Geophys. Res. Lett. 45(22), 12\u2013616 (2018)","journal-title":"Geophys. Res. Lett."},{"key":"27_CR34","doi-asserted-by":"crossref","unstructured":"Song, C., Lin, Y., Guo, S., Wan, H.: Spatial-temporal synchronous graph convolutional networks: A new framework for spatial-temporal network data forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence 34, pp. 914\u2013921 (2020)","DOI":"10.1609\/aaai.v34i01.5438"},{"key":"27_CR35","doi-asserted-by":"crossref","unstructured":"Wang, H., et al.: Gdi: a novel iot device identification framework via graph neural network-based tensor completion. IEEE Trans. Services Comput. (2024)","DOI":"10.1109\/TSC.2024.3463496"},{"key":"27_CR36","doi-asserted-by":"crossref","unstructured":"Wang, H., et al.: Easy begun is half done: Spatial-temporal graph modeling with st-curriculum dropout. In: Proceedings of the AAAI Conference on Artificial Intelligence 37(4), pp. 4668\u20134675 (2023)","DOI":"10.1609\/aaai.v37i4.25590"},{"issue":"2","key":"27_CR37","doi-asserted-by":"publisher","first-page":"1037","DOI":"10.1109\/TCYB.2022.3181810","volume":"54","author":"Z Wang","year":"2022","unstructured":"Wang, Z., et al.: A weighted symmetric graph embedding approach for link prediction in undirected graphs. IEEE Trans. Cybern. 54(2), 1037\u20131047 (2022)","journal-title":"IEEE Trans. Cybern."},{"key":"27_CR38","doi-asserted-by":"crossref","unstructured":"Man, W., Pan, S., Zhou, C., Chang, X., Zhu, X.: Unsupervised domain adaptive graph convolutional networks. In: Proceedings of The Web Conference 2020, pp. 1457\u20131467 (2020)","DOI":"10.1145\/3366423.3380219"},{"key":"27_CR39","doi-asserted-by":"crossref","unstructured":"Wu, Z., Pan, S., Long, G., Jiang, J., Chang, X., Zhang, C.: Connecting the dots: Multivariate time series forecasting with graph neural networks. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 753\u2013763 (2020)","DOI":"10.1145\/3394486.3403118"},{"key":"27_CR40","doi-asserted-by":"crossref","unstructured":"Wu, Z., Pan, S., Long, G., Jiang, J., Zhang, C.: Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121 (2019)","DOI":"10.24963\/ijcai.2019\/264"},{"key":"27_CR41","doi-asserted-by":"crossref","unstructured":"Xie, R., et al.: M2stl: Multi-range multi-level spatial-temporal learning model for network traffic prediction. IEEE Trans. Network Sci. Eng. (2024)","DOI":"10.1109\/TNSE.2024.3417371"},{"key":"27_CR42","doi-asserted-by":"crossref","unstructured":"Yan, Z., et al.: Multivariate time series forecasting exploiting tensor projection embedding and gated memory network. In: 2021 IEEE\/ACM 29th International Symposium on Quality of Service (IWQOS), pp. 1\u20136 (2021)","DOI":"10.1109\/IWQOS52092.2021.9521337"},{"key":"27_CR43","doi-asserted-by":"crossref","unstructured":"Yin, Y., et al.: Graphiot: lightweight iot device detection based on graph classifiers and incremental learning. IEEE Trans. Serv. Comput. (2024)","DOI":"10.1109\/TSC.2024.3466854"},{"key":"27_CR44","doi-asserted-by":"crossref","unstructured":"Zheng, C., Fan, X., Wang, C., Qi, J.: Gman: a graph multi-attention network for traffic prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence 34, pp. 1234\u20131241 (2020)","DOI":"10.1609\/aaai.v34i01.5477"},{"issue":"10","key":"27_CR45","doi-asserted-by":"publisher","first-page":"6329","DOI":"10.1109\/TCYB.2022.3163721","volume":"53","author":"YH Zhichao Zhou","year":"2022","unstructured":"Zhichao Zhou, Y.H., Zhang, Y., Chen, J., Cai, H.: Multiview deep graph infomax to achieve unsupervised graph embedding. IEEE Trans. Cybern. 53(10), 6329\u20136339 (2022)","journal-title":"IEEE Trans. Cybern."},{"key":"27_CR46","unstructured":"Zivot, E., Wang, J.: Vector autoregressive models for multivariate time series. Modeling financial time series with S-PLUS\u00ae, pp. 385\u2013429 (2006)"},{"issue":"2","key":"27_CR47","doi-asserted-by":"publisher","first-page":"913","DOI":"10.1007\/s10618-022-00903-7","volume":"37","author":"J Zuo","year":"2023","unstructured":"Zuo, J., Zeitouni, K., Taher, Y., Garcia-Rodriguez, S.: Graph convolutional networks for traffic forecasting with missing values. Data Min. Knowl. Disc. 37(2), 913\u2013947 (2023)","journal-title":"Data Min. Knowl. Disc."}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Security and Privacy in Communication Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-23456-8_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T11:24:27Z","timestamp":1777548267000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-23456-8_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032234551","9783032234568"],"references-count":47,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-23456-8_27","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"value":"1867-8211","type":"print"},{"value":"1867-822X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"24 April 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SecureComm","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Security and Privacy in Communication Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xiangtan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"securecomm2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/securecomm.eai-conferences.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}