{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T16:40:33Z","timestamp":1781282433591,"version":"3.54.1"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2022,5,24]],"date-time":"2022-05-24T00:00:00Z","timestamp":1653350400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,5,24]],"date-time":"2022-05-24T00:00:00Z","timestamp":1653350400000},"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":["Appl Intell"],"published-print":{"date-parts":[[2023,2]]},"DOI":"10.1007\/s10489-022-03412-8","type":"journal-article","created":{"date-parts":[[2022,5,24]],"date-time":"2022-05-24T07:02:52Z","timestamp":1653375772000},"page":"3183-3206","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":54,"title":["Robust anomaly-based intrusion detection system for in-vehicle network by graph neural network framework"],"prefix":"10.1007","volume":"53","author":[{"given":"Junchao","family":"Xiao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fuli","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongbo","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangxue","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,5,24]]},"reference":[{"issue":"2","key":"3412_CR1","doi-asserted-by":"publisher","first-page":"534","DOI":"10.1109\/TITS.2014.2320605","volume":"16","author":"S Tuohy","year":"2015","unstructured":"Tuohy S, Glavin M, Hughes C, Jones E, Trivedi M, Kilmartin L (2015) Intra-vehicle networks: A review. IEEE Trans Intell Transp Syst 16(2):534\u2013545. https:\/\/doi.org\/10.1109\/TITS.2014.2320605","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"3412_CR2","doi-asserted-by":"publisher","unstructured":"Fr\u00f6schle S, St\u00fchring A (2017) Analyzing the capabilities of the CAN attacker. In: Simon N, Foley DG, Snekkenes E (eds) Computer Security \u2013 ESORICS 2017, pp. 464\u2013482. Springer International Publishing, Cham. https:\/\/doi.org\/10.1007\/978-3-319-66402-6n_27","DOI":"10.1007\/978-3-319-66402-6n_27"},{"issue":"4","key":"3412_CR3","doi-asserted-by":"publisher","first-page":"1083","DOI":"10.1109\/TIFS.2018.2870826","volume":"14","author":"Marchetti M","year":"2019","unstructured":"Marchetti M, Stabili D (2019) READ: Reverse engineering of automotive data frames. IEEE Transactions on Information Forensics and Security 14(4):1083\u20131097. https:\/\/doi.org\/10.1109\/TIFS.2018.2870826https:\/\/doi.org\/10.1109\/TIFS.2018.2870826","journal-title":"IEEE Transactions on Information Forensics and Security"},{"issue":"2","key":"3412_CR4","doi-asserted-by":"publisher","first-page":"993","DOI":"10.1109\/TITS.2014.2351612 10.1109\/TITS.2014.2351612","volume":"16","author":"Woo S","year":"2015","unstructured":"Woo S, Jo HJ, Lee DH (2015) A practical wireless attack on the connected car and security protocol for in-vehicle CAN. IEEE Trans Intell Transp Syst 16(2):993\u20131006. https:\/\/doi.org\/10.1109\/TITS.2014.2351612https:\/\/doi.org\/10.1109\/TITS.2014.2351612","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"3412_CR5","doi-asserted-by":"publisher","first-page":"54607","DOI":"10.1109\/ACCESS.2018.2870695","volume":"6","author":"W Wu","year":"2018","unstructured":"Wu W, Kurachi R, Zeng G, Matsubara Y, Takada H, Li R, Li K (2018) IDH-CAN: A hardware-based ID hopping CAN mechanism with enhanced security for automotive real-time applications. IEEE Access 6:54607\u201354623. https:\/\/doi.org\/10.1109\/ACCESS.2018.2870695https:\/\/doi.org\/10.1109\/ACCESS.2018.2870695","journal-title":"IEEE Access"},{"key":"3412_CR6","doi-asserted-by":"crossref","unstructured":"Lin C, Sangiovanni-Vincentelli A (2012) Cyber-security for the controller area network (CAN) communication protocol. In: International Conference on Cyber Security, Washington, DC, USA, pp 1\u20137","DOI":"10.1109\/CyberSecurity.2012.7"},{"key":"3412_CR7","doi-asserted-by":"crossref","unstructured":"Nilsson DK, Larson UE, Jonsson E (2008) Efficient in-vehicle delayed data authentication based on compound message authentication codes. In: IEEE 68th Vehicular Technology Conference, Calgary, BC, Canada, pp 1\u20135","DOI":"10.1109\/VETECF.2008.259"},{"key":"3412_CR8","doi-asserted-by":"crossref","unstructured":"Wang E, Xu W, Sastry S, Liu S, Zeng K (2017) Hardware module-based message authentication in intra-vehicle networks. In: ACM\/IEEE International Conference on Cyber-Physical Systems, Pittsburgh, PA, USA, pp 207\u2013216","DOI":"10.1145\/3055004.3055016"},{"key":"3412_CR9","unstructured":"Bulck JV, M\u00fchlberg JT, Piessens F (2017) VulCAN: Efficient component authentication and software isolation for automotive control networks. In: Annual Computer Security Applications Conference, Orlando FL USA, pp 225\u2013237"},{"key":"3412_CR10","doi-asserted-by":"crossref","unstructured":"Lu Z, Wang Q, Chen X, Qu G, Lyu Y, Liu Z (2019) LEAP: A lightweight encryption and authentication protocol for in-vehicle communications. In: IEEE Intelligent Transportation Systems Conference, Auckland, New Zealand, pp 1158\u20131164","DOI":"10.1109\/ITSC.2019.8917500"},{"key":"3412_CR11","doi-asserted-by":"publisher","first-page":"490","DOI":"10.1016\/j.procs.2017.05.317","volume":"109C","author":"G Macher","year":"2017","unstructured":"Macher G, Sporer H, Brenner E, Kreiner C (2017) An automotive signal-layer security and trust-boundary identification approach. Procedia Computer Science 109C:490\u2013497. https:\/\/doi.org\/10.1016\/j.procs.2017.05.317https:\/\/doi.org\/10.1016\/j.procs.2017.05.317","journal-title":"Procedia Computer Science"},{"issue":"1","key":"3412_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5383\/JUSPN.10.01.001","volume":"10","author":"G Macher","year":"2018","unstructured":"Macher G, Sporer H, Brenner E, Kreiner C (2018) Signal-layer security and trust-boundary identification based on hardware-software interface definition. Journal of Ubiquitous Systems and Pervasive Networks 10(1):1\u20139. https:\/\/doi.org\/10.5383\/JUSPN.10.01.001https:\/\/doi.org\/10.5383\/JUSPN.10.01.001","journal-title":"Journal of Ubiquitous Systems and Pervasive Networks"},{"issue":"3","key":"3412_CR13","doi-asserted-by":"publisher","first-page":"919","DOI":"10.1109\/TITS.2019.2908074","volume":"21","author":"W Wu","year":"2020","unstructured":"Wu W, Li R, Xie G, An J, Bai Y, Zhou J, Li K (2020) A survey of intrusion detection for in-vehicle networks. IEEE Trans Intell Transp Syst 21(3):919\u2013933. https:\/\/doi.org\/10.1109\/tits.2019.2908074https:\/\/doi.org\/10.1109\/tits.2019.2908074","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"4","key":"3412_CR14","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1109\/MDAT.2016.2573598","volume":"33","author":"S Chakraborty","year":"2016","unstructured":"Chakraborty S, Al Faruque MA, Chang W, Goswami D, Wolf M, Zhu Q (2016) Automotive cyber-physical systems: A tutorial introduction. IEEE Design & Test 33 (4):92\u2013108. https:\/\/doi.org\/10.1109\/MDAT.2016.2573598https:\/\/doi.org\/10.1109\/MDAT.2016.2573598","journal-title":"IEEE Design & Test"},{"key":"3412_CR15","doi-asserted-by":"crossref","unstructured":"Wasicek A, Derler P, Lee EA (2014) Aspect-oriented modeling of attacks in automotive cyber-physical systems. In: ACM\/EDAC\/IEEE Design Automation Conference, San Francisco, CA, USA, pp 1\u20136","DOI":"10.1145\/2593069.2593095"},{"key":"3412_CR16","doi-asserted-by":"crossref","unstructured":"Abbott-McCune S, Shay LA (2016) Intrusion prevention system of automotive network CAN bus. In: IEEE International Carnahan Conference on Security Technology, Orlando, FL, USA, pp 1\u20138","DOI":"10.1109\/CCST.2016.7815711"},{"key":"3412_CR17","unstructured":"Malhotra P, Vig L, Shroff G, Agarwal P (2015) Long short term memory networks for anomaly detection in time series. In: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning , Bruges, Belgium, pp 89\u201394"},{"issue":"1, SI","key":"3412_CR18","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/s12530-020-09347-0","volume":"12","author":"A Protogerou","year":"2021","unstructured":"Protogerou A, Papadopoulos S, Drosou A, Tzovaras D, Refanidis I (2021) A graph neural network method for distributed anomaly detection in IoT. EVOLVING SYSTEMS 12(1, SI):19\u201336. https:\/\/doi.org\/10.1007\/s12530-020-09347-0","journal-title":"EVOLVING SYSTEMS"},{"issue":"1","key":"3412_CR19","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1109\/LCOMM.2019.2953722","volume":"24","author":"T Yu","year":"2020","unstructured":"Yu T, Wang X (2020) Topology verification enabled intrusion detection for in-vehicle CAN-FD networks. IEEE Commun Lett 24(1):227\u2013230. https:\/\/doi.org\/10.1109\/LCOMM.2019.2953722","journal-title":"IEEE Commun Lett"},{"key":"3412_CR20","doi-asserted-by":"publisher","first-page":"100291","DOI":"10.1016\/j.vehcom.2020.100291","volume":"27","author":"H Qin","year":"2021","unstructured":"Qin H, Yan M, Ji H (2021) Application of controller area network (CAN) bus anomaly detection based on time series prediction. Vehicular Communications 27:100291. https:\/\/doi.org\/10.1016\/j.vehcom.2020.100291https:\/\/doi.org\/10.1016\/j.vehcom.2020.100291","journal-title":"Vehicular Communications"},{"key":"3412_CR21","doi-asserted-by":"publisher","first-page":"37523","DOI":"10.1109\/ACCESS.2018.2848106","volume":"6","author":"H Ji","year":"2018","unstructured":"Ji H, Wang Y, Qin H, Wang Y, Li H (2018) Comparative performance evaluation of intrusion detection methods for in-vehicle networks. IEEE Access 6:37523\u201337532. https:\/\/doi.org\/10.1109\/ACCESS.2018.2848106https:\/\/doi.org\/10.1109\/ACCESS.2018.2848106","journal-title":"IEEE Access"},{"issue":"3","key":"3412_CR22","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1109\/MNET.2018.1700319","volume":"32","author":"X Li","year":"2018","unstructured":"Li X, Yu Y, Sun G, Chen K (2018) Connected vehicles\u2019 security from the perspective of the in-vehicle network. IEEE Netw 32(3):58\u201363. https:\/\/doi.org\/10.1109\/MNET.2018.1700319","journal-title":"IEEE Netw"},{"key":"3412_CR23","doi-asserted-by":"publisher","first-page":"1388:1","DOI":"10.3390\/sym11111388","volume":"11","author":"J Xiao","year":"2019","unstructured":"Xiao J, Wu H, Li X (2019) Internet of things meets vehicles: Sheltering in-vehicle network through lightweight machine learning. Symmetry 11:1388:1\u201321. https:\/\/doi.org\/10.3390\/sym11111388","journal-title":"Symmetry"},{"key":"3412_CR24","doi-asserted-by":"crossref","unstructured":"Xiao J, Wu H, Li X, Yuan L (2019) Practical IDS on in-vehicle network against diversified attack models. In: International Conference, Algorithms and Architectures for Parallel Processing, Melbourne, VIC, Australia, pp 456\u2013466","DOI":"10.1007\/978-3-030-38961-1_40"},{"key":"3412_CR25","doi-asserted-by":"crossref","unstructured":"Taylor A, Leblanc S, Japkowicz N (2016) Anomaly detection in automobile control network data with long short-term memory networks. In: IEEE International Conference on Data Science and Advanced Analytics, Montreal, QC, Canada, pp 130\u2013139","DOI":"10.1109\/DSAA.2016.20"},{"issue":"5","key":"3412_CR26","doi-asserted-by":"publisher","first-page":"4275","DOI":"10.1109\/TVT.2019.2907269","volume":"68","author":"K Zhu","year":"2019","unstructured":"Zhu K, Chen Z, Peng Y, Zhang L (2019) Mobile edge assisted literal multi-dimensional anomaly detection of in-vehicle network using LSTM. IEEE Trans Veh Technol 68(5):4275\u20134284. https:\/\/doi.org\/10.1109\/TVT.2019.2907269","journal-title":"IEEE Trans Veh Technol"},{"key":"3412_CR27","doi-asserted-by":"crossref","unstructured":"Xiao J, Wu H, Li X (2019) Robust and self-evolving IDS for in-vehicle network by enabling spatiotemporal information. In: IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems, Zhangjiajie, China, pp 1390\u20131397","DOI":"10.1109\/HPCC\/SmartCity\/DSS.2019.00193"},{"key":"3412_CR28","doi-asserted-by":"publisher","first-page":"100198:1","DOI":"10.1016\/j.vehcom.2019.100198","volume":"21","author":"HM Song","year":"2020","unstructured":"Song HM, Woo J, Kim HK (2020) In-vehicle network intrusion detection using deep convolutional neural network. Vehicular Communications 21:100198:1\u201313. https:\/\/doi.org\/10.1016\/j.vehcom.2019.100198https:\/\/doi.org\/10.1016\/j.vehcom.2019.100198","journal-title":"Vehicular Communications"},{"key":"3412_CR29","doi-asserted-by":"crossref","unstructured":"Kang M, Kang J (2016) A novel intrusion detection method using deep neural network for in-vehicle network security. In: IEEE Vehicular Technology Conference (VTC Spring), Nanjing, China, pp 1\u20135","DOI":"10.1109\/VTCSpring.2016.7504089"},{"key":"3412_CR30","doi-asserted-by":"publisher","first-page":"3934:1","DOI":"10.3390\/s20143934","volume":"20","author":"S Park","year":"2020","unstructured":"Park S, Choi J.-Y. (2020) Hierarchical anomaly detection model for in-vehicle networks using machine learning algorithms. Sensors 20:3934:1\u201321. https:\/\/doi.org\/10.3390\/s20143934","journal-title":"Sensors"},{"key":"3412_CR31","doi-asserted-by":"crossref","unstructured":"Marchetti M, Stabili D (2017) Anomaly detection of CAN bus messages through analysis of ID sequences. In: IEEE Intelligent Vehicles Symposium (IV), Los Angeles, CA, USA, pp 1577\u20131583","DOI":"10.1109\/IVS.2017.7995934"},{"key":"3412_CR32","doi-asserted-by":"crossref","unstructured":"Taylor A, Japkowicz N, Leblanc S (2015) Frequency-based anomaly detection for the automotive CAN bus. In: 2015 World Congress on Industrial Control Systems Security (WCICSS)","DOI":"10.1109\/WCICSS.2015.7420322"},{"key":"3412_CR33","doi-asserted-by":"publisher","unstructured":"Hoppe T, Kiltz S, Dittmann J (2008) Security threats to automotive CAN networks\u2013 practical examples and selected short-term countermeasures. In: Lect. Notes Comput. Sci. (Including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinform.), in: LNCS,. https:\/\/doi.org\/10.1007\/978-3-540-87698-4_21, vol 5219, pp 235\u2013248","DOI":"10.1007\/978-3-540-87698-4_21"},{"key":"3412_CR34","unstructured":"Valasek CMC (2013) Adventures in automotive networks and control units. Tech. White Pap, 99"},{"issue":"2","key":"3412_CR35","doi-asserted-by":"publisher","first-page":"1484","DOI":"10.1109\/TVT.2019.2961344","volume":"69","author":"H Olufowobi","year":"2020","unstructured":"Olufowobi H, Young C, Zambreno J, Bloom G (2020) SAIDuCANT: Specification-based automotive intrusion detection using controller area network (CAN) timing. IEEE Trans Veh Technol 69(2):1484\u20131494. https:\/\/doi.org\/10.1109\/TVT.2019.2961344","journal-title":"IEEE Trans Veh Technol"},{"issue":"6","key":"3412_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3362034","volume":"18","author":"J Zhou","year":"2019","unstructured":"Zhou J, Joshi P, Zeng H, Li R (2019) BTMonitor: Bit-time-based intrusion detection and attacker identification in controller area network. ACM Trans Embed Comput Syst 18(6):1\u201323. https:\/\/doi.org\/10.1145\/3362034","journal-title":"ACM Trans Embed Comput Syst"},{"key":"3412_CR37","doi-asserted-by":"publisher","first-page":"42422","DOI":"10.1109\/ACCESS.2020.2975893","volume":"8","author":"S Ohira","year":"2020","unstructured":"Ohira S, Desta AK, Arai I, Inoue H, Fujikawa K (2020) Normal and malicious sliding windows similarity analysis method for fast and accurate IDS against DoS attacks on in-vehicle networks. IEEE Access 8:42422\u201342435. https:\/\/doi.org\/10.1109\/access.2020.2975893https:\/\/doi.org\/10.1109\/access.2020.2975893","journal-title":"IEEE Access"},{"key":"3412_CR38","unstructured":"Shin KG, Cho KT (2017) Fingerprinting electronic control units for vehicle intrusion detection"},{"issue":"8","key":"3412_CR39","doi-asserted-by":"publisher","first-page":"2114","DOI":"10.1109\/TIFS.2018.2812149","volume":"13","author":"W Choi","year":"2018","unstructured":"Choi W, Joo K, Jo HJ, Park MC, Lee DH (2018) VoltageIDS: Low-level communication characteristics for automotive intrusion detection system. IEEE Transactions on Information Forensics and Security 13(8):2114\u20132129. https:\/\/doi.org\/10.1109\/TIFS.2018.2812149https:\/\/doi.org\/10.1109\/TIFS.2018.2812149","journal-title":"IEEE Transactions on Information Forensics and Security"},{"key":"3412_CR40","doi-asserted-by":"publisher","first-page":"54979","DOI":"10.1109\/ACCESS.2020.2980523","volume":"8","author":"S Katragadda","year":"2020","unstructured":"Katragadda S, Darby PJ, Roche A, Gottumukkala R (2020) Detecting low-rate replay-based injection attacks on in-vehicle networks. IEEE Access 8:54979\u201354993. https:\/\/doi.org\/10.1109\/ACCESS.2020.2980523https:\/\/doi.org\/10.1109\/ACCESS.2020.2980523","journal-title":"IEEE Access"},{"key":"3412_CR41","doi-asserted-by":"crossref","unstructured":"Song HM, Kim HR, Kim HK (2016) Intrusion detection system based on the analysis of time intervals of CAN messages for in-vehicle network. In: International Conference on Information Networking, Kota Kinabalu, Malaysia, pp 63\u201368","DOI":"10.1109\/ICOIN.2016.7427089"},{"key":"3412_CR42","doi-asserted-by":"crossref","unstructured":"Cho KT, Kang GS (2017) Viden: Attacker identification on in-vehicle networks. In: ACM SIGSAC Conference on Computer and Communications Security, Dallas Texas USA, pp 1109\u20131123","DOI":"10.1145\/3133956.3134001"},{"key":"3412_CR43","doi-asserted-by":"publisher","first-page":"101857:1","DOI":"10.1016\/j.cose.2020.101857","volume":"94","author":"S Tariq","year":"2020","unstructured":"Tariq S, Lee S, Kim HK, Woo SS (2020) CAN-ADF: The controller area network attack detection framework. Computers & Security 94:101857:1\u201312. https:\/\/doi.org\/10.1016\/j.cose.2020.101857https:\/\/doi.org\/10.1016\/j.cose.2020.101857","journal-title":"Computers & Security"},{"issue":"1","key":"3412_CR44","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","volume":"32","author":"Z Wu","year":"2021","unstructured":"Wu Z, Pan S, Chen F, Long G, Zhang C, Yu PS (2021) A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems 32(1):4\u201324. https:\/\/doi.org\/10.1109\/TNNLS.2020.2978386","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"3412_CR45","doi-asserted-by":"publisher","unstructured":"Nathani D, Chauhan J, Sharma C, Kaul M (2019) Learning attention-based embeddings for relation prediction in knowledge graphs. arXiv:1906.01195, https:\/\/doi.org\/10.18653\/v1\/P19-1466","DOI":"10.18653\/v1\/P19-1466"},{"key":"3412_CR46","doi-asserted-by":"publisher","unstructured":"Wu Z, Pan S, Chen F, Long G, Zhang C, Yu PS (2019) A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems, pp 1\u201321, https:\/\/doi.org\/10.1109\/TNNLS.2020.2978386","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"3412_CR47","doi-asserted-by":"crossref","unstructured":"Lee H, Jeong SH, Kim HK (2017) OTIDS: A novel intrusion detection system for in-vehicle network by using remote frame. In: Annual Conference on Privacy, Security and Trust, Calgary, AB, Canada, pp 57\u201366","DOI":"10.1109\/PST.2017.00017"},{"key":"3412_CR48","doi-asserted-by":"publisher","unstructured":"Xie L, Pi D, Zhang X, Chen J, Luo Y, Yu W (2021) Graph neural network approach for anomaly detection. MEASUREMENT, 180, https:\/\/doi.org\/10.1016\/j.measurement.2021.109546","DOI":"10.1016\/j.measurement.2021.109546"},{"key":"3412_CR49","unstructured":"Kipf T, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv:1609.02907"},{"key":"3412_CR50","unstructured":"Veli\u010dkovi\u010d P, Cucurull G, Casanova A, Romero A, Li\u00f2 P, Bengio Y (2017) Graph attention networks. arXiv:1710.10903"},{"key":"3412_CR51","doi-asserted-by":"crossref","unstructured":"Linghu Y, Li X (2021) Wsg-inv: Weighted state graph model for intrusion detection on in-vehicle network. In: 2021 IEEE Wireless Communications and Networking Conference (WCNC), pp 1\u20137","DOI":"10.1109\/WCNC49053.2021.9417552"},{"key":"3412_CR52","unstructured":"Hamilton LW, Ying R, Leskovec J. (2017) Inductive representation learning on large graphs. In: International Conference on Neural Information Processing Systems. Curran Associates Inc., Red Hook, NY, USA, pp 1025\u20131035"},{"key":"3412_CR53","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. In: International Conference on Neural Information Processing Systems. Curran Associates Inc., Red Hook, NY, USA, pp 6000\u20136010"},{"key":"3412_CR54","unstructured":"Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv:1412.6980"},{"issue":"6","key":"3412_CR55","doi-asserted-by":"publisher","first-page":"508","DOI":"10.1186\/cc3000","volume":"8","author":"V Bewick","year":"2004","unstructured":"Bewick V, Cheek L, Ball J (2004) Statistics review 13: Receiver operating characteristic curves. Critical Care 8(6):508\u2013512. https:\/\/doi.org\/10.1186\/cc3000","journal-title":"Critical Care"},{"issue":"2","key":"3412_CR56","doi-asserted-by":"publisher","first-page":"121","DOI":"10.3233\/MAS-130284","volume":"9","author":"S Pundir","year":"2014","unstructured":"Pundir S, Amala R (2014) Parametric receiver operating characteristic modeling for continuous data: A glance. Model Assist Stat Appl 9(2):121\u2013135. https:\/\/doi.org\/10.3233\/MAS-130284","journal-title":"Model Assist Stat Appl"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03412-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-022-03412-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03412-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T11:56:13Z","timestamp":1673438173000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-022-03412-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,24]]},"references-count":56,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,2]]}},"alternative-id":["3412"],"URL":"https:\/\/doi.org\/10.1007\/s10489-022-03412-8","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,24]]},"assertion":[{"value":"17 February 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 May 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}