{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T18:35:03Z","timestamp":1743014103653,"version":"3.40.3"},"publisher-location":"Cham","reference-count":47,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030727918"},{"type":"electronic","value":"9783030727925"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-72792-5_53","type":"book-chapter","created":{"date-parts":[[2021,4,26]],"date-time":"2021-04-26T20:34:22Z","timestamp":1619469262000},"page":"681-693","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hyperparameter Analysis of Temporal Graph Convolutional Network Model Applied to Traffic Prediction"],"prefix":"10.1007","author":[{"given":"Jing","family":"Huang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan","family":"An","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kailiang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ping","family":"Cui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,4,27]]},"reference":[{"key":"53_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/s11036-019-01415-3","author":"K Zhang","year":"2019","unstructured":"Zhang, K., Chen, L., An, Y., et al.: A QoE test system for vehicular voice cloud services. Mob. Netw. Appl. (2019). https:\/\/doi.org\/10.1007\/s11036-019-01415-3","journal-title":"Mob. Netw. Appl."},{"issue":"1","key":"53_CR2","first-page":"196","volume":"7","author":"F Wang","year":"2019","unstructured":"Wang, F., Jiang, D., Qi, S.: An adaptive routing algorithm for integrated information networks. China Commun. 7(1), 196\u2013207 (2019)","journal-title":"China Commun."},{"key":"53_CR3","doi-asserted-by":"crossref","unstructured":"Huo, L., Jiang, D., Lv, Z., et al.: An intelligent optimization-based traffic information acquirement approach to software-defined networking. Comput. Intell. 1\u201321 (2019)","DOI":"10.1111\/coin.12250"},{"issue":"5","key":"53_CR4","doi-asserted-by":"publisher","first-page":"1079","DOI":"10.1049\/cje.2017.07.018","volume":"26","author":"L Chen","year":"2017","unstructured":"Chen, L., Jiang, D., Bao, R., Xiong, J., Liu, F., Bei, L.: MIMO Scheduling effectiveness analysis for bursty data service from view of QoE. Chin. J. Electron. 26(5), 1079\u20131085 (2017)","journal-title":"Chin. J. Electron."},{"key":"53_CR5","unstructured":"Jiang, D., Wang, Y., Lv, Z., et al.: Big data analysis-based network behavior insight of cellular networks for industry 4.0 applications. IEEE Trans. Ind. Inform. 16(2), 1310\u20131320 (2020)"},{"issue":"1","key":"53_CR6","first-page":"1","volume":"1","author":"D Jiang","year":"2018","unstructured":"Jiang, D., Huo, L., Song, H.: Rethinking behaviors and activities of base stations in mobile cellular networks based on big data analysis. IEEE Trans. Netw. Sci. Eng. 1(1), 1\u201312 (2018)","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"issue":"1","key":"53_CR7","doi-asserted-by":"publisher","first-page":"15408","DOI":"10.1109\/ACCESS.2018.2794354","volume":"6","author":"L Chen","year":"2018","unstructured":"Chen, L., et al.: A lightweight end-side user experience data collection system for quality evaluation of multimedia communications. IEEE Access 6(1), 15408\u201315419 (2018)","journal-title":"IEEE Access"},{"key":"53_CR8","doi-asserted-by":"publisher","DOI":"10.1007\/s11036-019-01414-4","author":"L Chen","year":"2020","unstructured":"Chen, L., Zhang, L.: Spectral efficiency analysis for massive MIMO system under QoS constraint: an effective capacity perspective. Mob. Netw. Appl. (2020). https:\/\/doi.org\/10.1007\/s11036-019-01414-4","journal-title":"Mob. Netw. Appl."},{"key":"53_CR9","doi-asserted-by":"crossref","unstructured":"Wang, F., Jiang, D., Qi, S., et al.: A dynamic resource scheduling scheme in edge computing satellite networks. Mob. Netw. Appl. 1\u201312 (2019)","DOI":"10.1007\/s11036-019-01421-5"},{"issue":"10","key":"53_CR10","doi-asserted-by":"publisher","first-page":"3305","DOI":"10.1109\/TITS.2017.2778939","volume":"19","author":"D Jiang","year":"2018","unstructured":"Jiang, D., Huo, L., Lv, Z., et al.: A joint multi-criteria utility-based network selection approach for vehicle-to-infrastructure networking. IEEE Trans. Intell. Transp. Syst. 19(10), 3305\u20133319 (2018)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"6","key":"53_CR11","doi-asserted-by":"publisher","first-page":"1437","DOI":"10.1109\/JIOT.2016.2613111","volume":"3","author":"D Jiang","year":"2016","unstructured":"Jiang, D., Zhang, P., Lv, Z., et al.: Energy-efficient multi-constraint routing algorithm with load balancing for smart city applications. IEEE Internet Things J. 3(6), 1437\u20131447 (2016)","journal-title":"IEEE Internet Things J."},{"key":"53_CR12","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1016\/j.neucom.2016.07.056","volume":"220","author":"D Jiang","year":"2017","unstructured":"Jiang, D., Li, W., Lv, H.: An energy-efficient cooperative multicast routing in multi-hop wireless networks for smart medical applications. Neurocomputing 220, 160\u2013169 (2017)","journal-title":"Neurocomputing"},{"key":"53_CR13","unstructured":"Jiang, D., Wang, Y., Lv, Z., et al.: Intelligent optimization-based reliable energy-efficient networking in cloud services for IIoT networks. IEEE J. Select. Areas Commun. 1\u20136 (2019)"},{"issue":"3","key":"53_CR14","first-page":"1","volume":"5","author":"D Jiang","year":"2018","unstructured":"Jiang, D., Wang, W., Shi, L., et al.: A compressive sensing-based approach to end-to-end network traffic reconstruction. IEEE Trans. Netw. Sci. Eng. 5(3), 1\u201312 (2018)","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"issue":"5","key":"53_CR15","first-page":"1","volume":"13","author":"D Jiang","year":"2018","unstructured":"Jiang, D., Huo, L., Li, Y.: Fine-granularity inference and estimations to network traffic for SDN. PLoS ONE 13(5), 1\u201323 (2018)","journal-title":"PLoS ONE"},{"key":"53_CR16","doi-asserted-by":"crossref","unstructured":"Wang, Y., Jiang, D., Huo, L., et al.: A new traffic prediction algorithm to software defined networking. Mob. Netw. Appl. 1\u201310 (2019)","DOI":"10.1007\/s11036-019-01423-3"},{"key":"53_CR17","doi-asserted-by":"crossref","unstructured":"Qi, S., Jiang, D., Huo, L.: A prediction approach to end-to-end traffic in space information networks. Mob. Netw. Appl. 1\u201310 (2019)","DOI":"10.1007\/s11036-019-01424-2"},{"key":"53_CR18","doi-asserted-by":"crossref","unstructured":"Huo, L., Jiang, D., Qi, S., et al.: An AI-based adaptive cognitive modeling and measurement method of network traffic for EIS. Mob. Netw. Appl. 1\u201311 (2019)","DOI":"10.1007\/s11036-019-01419-z"},{"key":"53_CR19","doi-asserted-by":"crossref","unstructured":"Huo, L., Jiang, D., Zhu, X., et al.: An SDN-based fine-grained measurement and modeling approach to vehicular communication network traffic. Int. J. Commun. Syst. 1\u201312, (2019)","DOI":"10.1002\/dac.4092"},{"issue":"7587","key":"53_CR20","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1038\/nature16961","volume":"529","author":"D Silver","year":"2016","unstructured":"Silver, D., et al.: Mastering the game of Go with deep neural networks and tree search. Nature 529(7587), 484\u2013489 (2016)","journal-title":"Nature"},{"issue":"7676","key":"53_CR21","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1038\/nature24270","volume":"550","author":"D Silver","year":"2017","unstructured":"Silver, D., et al.: Mastering the game of go without human knowledge. Nature 550(7676), 354\u2013359 (2017)","journal-title":"Nature"},{"issue":"6337","key":"53_CR22","doi-asserted-by":"publisher","first-page":"508","DOI":"10.1126\/science.aam6960","volume":"356","author":"M Morav\u02c7cik","year":"2017","unstructured":"Morav\u02c7cik, M., et al.: DeepStack: Expert-level artifificial intelligence in heads-up no-limit poker. Science 356(6337), 508\u2013513 (2017)","journal-title":"Science"},{"key":"53_CR23","unstructured":"Park, D., Rilett, L.R.: Forecasting freeway link travel times with a multilayer feedforward neural network. Comput.-Aided Civil Infrastruct. Eng. 14(5), 357\u2013367 (1999)"},{"issue":"5","key":"53_CR24","doi-asserted-by":"publisher","first-page":"2191","DOI":"10.1109\/TITS.2014.2311123","volume":"15","author":"W Huang","year":"2014","unstructured":"Huang, W., Song, G., Hong, H., Xie, K.: Deep architecture for traffific flow prediction: Deep belief networks with multitask learning. IEEE Trans. Intell. Transp. Syst. 15(5), 2191\u20132201 (2014)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"53_CR25","doi-asserted-by":"crossref","unstructured":"Fu, R., Zhang, Z., Li, L.: Using LSTM and GRU neural network methods for traffific flow prediction. In: 31st Youth Academic Annual Conference China Association Automation (YAC), Wuhan, China, pp. 324\u2013328 (2016)","DOI":"10.1109\/YAC.2016.7804912"},{"issue":"1","key":"53_CR26","doi-asserted-by":"publisher","first-page":"30","DOI":"10.3141\/1811-04","volume":"1811","author":"JWC Van Lint","year":"2002","unstructured":"Van Lint, J.W.C., Hoogendoorn, S.P., van Zuylen, H.J.: Freeway travel time prediction with state-space neural networks: modeling statespace dynamics with recurrent neural networks. Transp. Res. Rec. 1811(1), 30\u201339 (2002)","journal-title":"Transp. Res. Rec."},{"key":"53_CR27","unstructured":"Zhao, L., Song, Y., Zhang, C., et al.: T-GCN: a temporal graph convolutional network for traffic prediction. IEEE Trans. Intell. Transp. Syst. 21(9), 3848\u20133858 (2018)"},{"key":"53_CR28","doi-asserted-by":"crossref","unstructured":"Ding, L., Huang, Z., Chen, G.: An FPGA implementation of GCN with sparse adjacency matrix. In: 2019 IEEE 13th International Conference on ASIC (ASICON) (2019)","DOI":"10.1109\/ASICON47005.2019.8983647"},{"key":"53_CR29","doi-asserted-by":"crossref","unstructured":"Zheng, J., Li, D.: GCN-TC: combining trace graph with statistical features for network traffic classification. In: 2019 IEEE International Conference on Communications (ICC) (2019)","DOI":"10.1109\/ICC.2019.8761115"},{"key":"53_CR30","doi-asserted-by":"crossref","unstructured":"Li, Z., Xiong, G., Chen, Y.: A hybrid deep learning approach with GCN and LSTM for traffic flow prediction. In: 2019 IEEE Intelligent Transportation Systems Conference (ITSC) (2019)","DOI":"10.1109\/ITSC.2019.8916778"},{"key":"53_CR31","unstructured":"Reddi, S.J., Kale, S., Kumar, S.: On the Convergence of Adam and Beyond (2019)"},{"key":"53_CR32","unstructured":"Keskar, N.S., Socher, R.: Improving Generalization Performance by Switching from Adam to SGD (2017)"},{"key":"53_CR33","unstructured":"Hoffer, E., Hubara, I., Soudrym D.: Train longer, generalize better: closing the generalization gap in large batch training of neural networks. In: Advances in Neural Information Processing Systems, pp. 1731\u20131741 (2017)"},{"key":"53_CR34","unstructured":"Goyal, P., Dollar, P., Girshick, R.B., et al.: Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour. arXiv: Computer Vision and Pattern Recognition (2017)"},{"key":"53_CR35","unstructured":"Keskar, N.S., Socher, R.: Improving generalization performance by switching from adam to sgd. arXiv preprint arXiv:1712.07628 (2017)"},{"key":"53_CR36","unstructured":"Reddi, S.J., Kale, S., Kumar, S.: On the convergence of adam and beyond (2018)"},{"key":"53_CR37","doi-asserted-by":"crossref","unstructured":"Smith, L.N.: Cyclical learning rates for training neural networks. In: 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 464\u2013472, IEEE (2017)","DOI":"10.1109\/WACV.2017.58"},{"key":"53_CR38","unstructured":"Smith, S.L., Kindermans, P.J., Ying, C., et al.: Don\u2019t decay the learning rate, increase the batch size. arXiv preprint arXiv:1711.00489 (2017)"},{"key":"53_CR39","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1007\/978-3-319-75193-1_18","volume-title":"Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications","author":"AF Cardona-Escobar","year":"2018","unstructured":"Cardona-Escobar, A.F., Giraldo-Forero, A.F., Castro-Ospina, A.E., Jaramillo-Garz\u00f3n, F.A.: Efficient hyperparameter optimization in convolutional neural networks by learning curves prediction. In: Mendoza, M., Velast\u00edn, S. (eds.) CIARP 2017. LNCS, vol. 10657, pp. 143\u2013151. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-75193-1_18"},{"key":"53_CR40","unstructured":"Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral fifiltering. Proc. Adv. Neural Inf. Process. Syst., 3844\u20133852 (2016)"},{"key":"53_CR41","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classifification with graph convolutional networks (2016). https:\/\/arxiv.org\/abs\/1609.02907"},{"key":"53_CR42","unstructured":"Bruna, J., Zaremba, W., Szlam, A., Lecun, Y.: Spectral networks and locally connected networks on graphs. https:\/\/arxiv.org\/abs\/1312.6203 (2013)"},{"key":"53_CR43","unstructured":"Ma, Y., et al: High performance graph convolutional networks with applications in testability Analysis. In: ACM\/IEEE Design Automation Conference (DAC), Las Vegas, NV, pp. 18:1\u201318:6 (2019)"},{"key":"53_CR44","unstructured":"Forecasting road traffic speeds by considering area-wide spatio temporal dependencies based on a graph convolutional neural network (GCN). In: 2019 Chinese Control Conference (CCC) (2019)"},{"key":"53_CR45","doi-asserted-by":"crossref","unstructured":"Wu, C., Chai, L., Yang, J., Sheng, Y.: Facial expression recognition using convolutional neural network on graphs. In: The 38th China Control Conference, pp. 90\u201394 (2019)","DOI":"10.23919\/ChiCC.2019.8866311"},{"key":"53_CR46","doi-asserted-by":"crossref","unstructured":"Cho, K., van Merrienboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: Encoder-decoder approaches. https:\/\/arxiv.org\/abs\/1409.1259 (2014)","DOI":"10.3115\/v1\/W14-4012"},{"key":"53_CR47","unstructured":"Chung, J., Gulcehre, C., Cho, K.H., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. https:\/\/arxiv.org\/abs\/1412.3555 (2014)"}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Simulation Tools and Techniques"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-72792-5_53","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,26]],"date-time":"2021-04-26T22:40:48Z","timestamp":1619476848000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-72792-5_53"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030727918","9783030727925"],"references-count":47,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-72792-5_53","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"type":"print","value":"1867-8211"},{"type":"electronic","value":"1867-822X"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"27 April 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SIMUtools","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Simulation Tools and Techniques","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guiyang","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":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"simutools2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/simutools.eai-conferences.org\/2020\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Confy +","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"354","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"125","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"35% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Due to COVID 19 pandemic the conference was held virtually.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}