{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T15:51:20Z","timestamp":1778860280938,"version":"3.51.4"},"reference-count":338,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2023,4,5]],"date-time":"2023-04-05T00:00:00Z","timestamp":1680652800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"DARPA Warfighter Analytics using Smartphones for Health (WASH) program"},{"name":"National Cancer Institute of the National Institutes of Health","award":["R01CA239246"],"award-info":[{"award-number":["R01CA239246"]}]},{"name":"University of Virginia Engineering in Medicine Seed Award"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Sen. Netw."],"published-print":{"date-parts":[[2023,5,31]]},"abstract":"<jats:p>The Internet of Things (IoT) boom has revolutionized almost every corner of people\u2019s daily lives: healthcare, environment, transportation, manufacturing, supply chain, and so on. With the recent development of sensor and communication technology, IoT artifacts, including smart wearables, cameras, smartwatches, and autonomous systems can accurately measure and perceive their surrounding environment. Continuous sensing generates massive amounts of data and presents challenges for machine learning. Deep learning models (e.g., convolution neural networks and recurrent neural networks) have been extensively employed in solving IoT tasks by learning patterns from multi-modal sensory data. Graph neural networks (GNNs), an emerging and fast-growing family of neural network models, can capture complex interactions within sensor topology and have been demonstrated to achieve state-of-the-art results in numerous IoT learning tasks. In this survey, we present a comprehensive review of recent advances in the application of GNNs to the IoT field, including a deep dive analysis of GNN design in various IoT sensing environments, an overarching list of public data and source codes from the collected publications, and future research directions. To keep track of newly published works, we collect representative papers and their open-source implementations and create a Github repository at GNN4IoT.<\/jats:p>","DOI":"10.1145\/3565973","type":"journal-article","created":{"date-parts":[[2022,10,7]],"date-time":"2022-10-07T13:21:49Z","timestamp":1665148909000},"page":"1-50","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":160,"title":["Graph Neural Networks in IoT: A Survey"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3908-8391","authenticated-orcid":false,"given":"Guimin","family":"Dong","sequence":"first","affiliation":[{"name":"Amazon and University of Virginia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1326-5553","authenticated-orcid":false,"given":"Mingyue","family":"Tang","sequence":"additional","affiliation":[{"name":"University of Virginia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5990-9193","authenticated-orcid":false,"given":"Zhiyuan","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Virginia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0628-1416","authenticated-orcid":false,"given":"Jiechao","family":"Gao","sequence":"additional","affiliation":[{"name":"University of Virginia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4764-3359","authenticated-orcid":false,"given":"Sikun","family":"Guo","sequence":"additional","affiliation":[{"name":"University of Virginia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0136-9857","authenticated-orcid":false,"given":"Lihua","family":"Cai","sequence":"additional","affiliation":[{"name":"University of Virginia and South China Normal University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4683-8012","authenticated-orcid":false,"given":"Robert","family":"Gutierrez","sequence":"additional","affiliation":[{"name":"University of Virginia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4103-8107","authenticated-orcid":false,"given":"Bradford","family":"Campbel","sequence":"additional","affiliation":[{"name":"University of Virginia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4956-8214","authenticated-orcid":false,"given":"Laura E.","family":"Barnes","sequence":"additional","affiliation":[{"name":"University of Virginia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6295-2523","authenticated-orcid":false,"given":"Mehdi","family":"Boukhechba","sequence":"additional","affiliation":[{"name":"University of Virginia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,4,5]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"2022. USGS water data for the nation. https:\/\/waterdata.usgs.gov\/nwis."},{"key":"e_1_3_2_3_2","unstructured":"2023. Water Quality Data Home. http:\/\/www.waterqualitydata.us\/."},{"key":"e_1_3_2_4_2","unstructured":"2015. ECML\/PKDD 15: Taxi trajectory prediction (i). https:\/\/www.kaggle.com\/c\/pkdd-15-predict-taxi-service-trajectory-i."},{"key":"e_1_3_2_5_2","unstructured":"2022. Historical data of air quality in China: Historical data of meteorology in China: Historical data of air quality in Beijing. https:\/\/quotsoft.net\/air\/."},{"key":"e_1_3_2_6_2","unstructured":"2017. Interstate 80 freeway dataset FHWA-HRT-06-137. Retrieved from https:\/\/www.fhwa.dot.gov\/publications\/research\/operations\/06137\/."},{"key":"e_1_3_2_7_2","unstructured":"2023. MotionNode miniature Inertial Measurement Unit. Retrieved from https:\/\/www.motionnode.com\/."},{"key":"e_1_3_2_8_2","unstructured":"2019. Pecanstreet Dataport. Retrieved from https:\/\/dataport.pecanstreet.org\/."},{"key":"e_1_3_2_9_2","unstructured":"2022. TLC Trip Record Data. Retrieved from https:\/\/www1.nyc.gov\/site\/tlc\/about\/tlc-trip-record-data.page."},{"key":"e_1_3_2_10_2","unstructured":"2023. Urban computing. Retrieved from http:\/\/urban-computing.com\/."},{"key":"e_1_3_2_11_2","unstructured":"2017. US Highway 101 dataset FHWA-HRT-07-030. Retrieved from https:\/\/www.fhwa.dot.gov\/publications\/research\/operations\/07030\/."},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.21227\/H2QP48"},{"key":"e_1_3_2_13_2","unstructured":"2022. Global Data Assimilation System. Retrieved from https:\/\/www.ncei.noaa.gov\/products\/weather-climate-models\/global-data-assimilation."},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2021.3076974"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.3390\/s21144758"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2020.2988293"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.110"},{"key":"e_1_3_2_18_2","first-page":"946","volume-title":"Proceedings of the 2016 8th International Conference on Ubiquitous and Future Networks (ICUFN)","author":"Ali Mouhannad","year":"2016","unstructured":"Mouhannad Ali, Ahmad Haj Mosa, Fadi Al Machot, and Kyandoghere Kyamakya. 2016. EEG-based emotion recognition approach for e-healthcare applications. In Proceedings of the 2016 8th International Conference on Ubiquitous and Future Networks (ICUFN). IEEE, 946\u2013950."},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.comcom.2017.10.003"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/IROS51168.2021.9636377"},{"key":"e_1_3_2_21_2","first-page":"327","volume-title":"Proceedings of the 2017 13th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","author":"Alzahrani Mona Saleh","year":"2017","unstructured":"Mona Saleh Alzahrani, Salma Kammoun Jarraya, Manar Ali Salamah, and Han\u00eane Ben-Abdallah. 2017. FallFree: Multiple fall scenario dataset of cane users for monitoring applications using kinect. In Proceedings of the 2017 13th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS). IEEE, 327\u2013333."},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2892405"},{"key":"e_1_3_2_23_2","first-page":"91","volume-title":"Proceedings of the International Workshop on Ambient Assisted Living","author":"Banos Oresti","year":"2014","unstructured":"Oresti Banos, Rafael Garcia, Juan A. Holgado-Terriza, Miguel Damas, Hector Pomares, Ignacio Rojas, Alejandro Saez, and Claudia Villalonga. 2014. mHealthDroid: A novel framework for agile development of mobile health applications. In Proceedings of the International Workshop on Ambient Assisted Living. Springer, 91\u201398."},{"key":"e_1_3_2_24_2","unstructured":"Peter W. Battaglia Jessica B. Hamrick Victor Bapst Alvaro Sanchez-Gonzalez Vinicius Zambaldi Mateusz Malinowski Andrea Tacchetti David Raposo Adam Santoro Ryan Faulkner Caglar Gulcehre Francis Song Andrew Ballard Justin Gilmer George Dahl Ashish Vaswani Kelsey Allen Charles Nash Victoria Langston Chris Dyer Nicolas Heess Daan Wierstra Pushmeet Kohli Matt Botvinick Oriol Vinyals Yujia Li and Razvan Pascanu. 2018. Relational inductive biases deep learning and graph networks. arXiv:1806.01261. Retrieved from https:\/\/arxiv.org\/abs\/1806.01261."},{"key":"e_1_3_2_25_2","unstructured":"Rianne van den Berg Thomas N. Kipf and Max Welling. 2017. Graph convolutional matrix completion. arXiv:1706.02263. Retrieved from https:\/\/arxiv.org\/abs\/1706.02263."},{"key":"e_1_3_2_26_2","first-page":"491","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Bi Yin","year":"2019","unstructured":"Yin Bi, Aaron Chadha, Alhabib Abbas, Eirina Bourtsoulatze, and Yiannis Andreopoulos. 2019. Graph-based object classification for neuromorphic vision sensing. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. 491\u2013501."},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1007\/s41109-021-00438-8"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.2196\/10101"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.smhl.2018.07.005"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1145\/3123024.3125607"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSYST.2021.3109082"},{"key":"e_1_3_2_32_2","unstructured":"Greg Brockman Vicki Cheung Ludwig Pettersson Jonas Schneider John Schulman Jie Tang and Wojciech Zaremba. 2016. OpenAI Gym. arXiv:1606.01540. Retrieved from https:\/\/arxiv.org\/abs\/1606.01540."},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/IROS51168.2021.9636376"},{"key":"e_1_3_2_34_2","first-page":"18\u2013es","volume-title":"Proceedings of the 2nd Annual International Workshop on Wireless Internet","author":"Campbell Andrew T.","year":"2006","unstructured":"Andrew T. Campbell, Shane B. Eisenman, Nicholas D. Lane, Emiliano Miluzzo, and Ronald A. Peterson. 2006. People-centric urban sensing. In Proceedings of the 2nd Annual International Workshop on Wireless Internet. 18\u2013es."},{"key":"e_1_3_2_35_2","first-page":"1","volume-title":"Proceedings of the 2018 29th Irish Signals and Systems Conference (ISSC)","author":"Campbell Sean","year":"2018","unstructured":"Sean Campbell, Niall O\u2019Mahony, Lenka Krpalcova, Daniel Riordan, Joseph Walsh, Aidan Murphy, and Conor Ryan. 2018. Sensor technology in autonomous vehicles: A review. In Proceedings of the 2018 29th Irish Signals and Systems Conference (ISSC). IEEE, 1\u20134."},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/595"},{"key":"e_1_3_2_37_2","doi-asserted-by":"crossref","first-page":"9491","DOI":"10.1109\/ICRA40945.2020.9196697","volume-title":"Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA)","author":"Casas Sergio","year":"2020","unstructured":"Sergio Casas, Cole Gulino, Renjie Liao, and Raquel Urtasun. 2020. Spagnn: Spatially-aware graph neural networks for relational behavior forecasting from sensor data. In Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 9491\u20139497."},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/5174815"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2021.3078433"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/5397814"},{"key":"e_1_3_2_41_2","first-page":"518","volume-title":"Proceedings of the Telecom","volume":"2","author":"Chen Aaron","year":"2021","unstructured":"Aaron Chen, Jeffrey Law, and Michal Aibin. 2021. A survey on traffic prediction techniques using artificial intelligence for communication networks. In Proceedings of the Telecom, Vol. 2. Multidisciplinary Digital Publishing Institute, 518\u2013535."},{"key":"e_1_3_2_42_2","first-page":"485","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"33","author":"Chen Cen","year":"2019","unstructured":"Cen Chen, Kenli Li, Sin G. Teo, Xiaofeng Zou, Kang Wang, Jie Wang, and Zeng Zeng. 2019. Gated residual recurrent graph neural networks for traffic prediction. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 485\u2013492."},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1109\/CASE49439.2021.9551450"},{"key":"e_1_3_2_44_2","unstructured":"Mengyuan Chen Jiang Zhang Zhang Zhang Lun Du Qiao Hu Shuo Wang and Jiaqi Zhu. 2020. Inference for network structure and dynamics from time series data via graph neural network. arXiv:2001.06576. Retrieved from https:\/\/arxiv.org\/abs\/2001.06576."},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1111\/mice.12702"},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2021.3068103"},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA48506.2021.9560788"},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.1145\/2020408.2020579"},{"key":"e_1_3_2_49_2","first-page":"606","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"34","author":"Choi Edward","year":"2020","unstructured":"Edward Choi, Zhen Xu, Yujia Li, Michael Dusenberry, Gerardo Flores, Emily Xue, and Andrew Dai. 2020. Learning the graphical structure of electronic health records with graph convolutional transformer. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 606\u2013613."},{"key":"e_1_3_2_50_2","unstructured":"Junyoung Chung Caglar Gulcehre KyungHyun Cho and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv:1412.3555. Retrieved from https:\/\/arxiv.org\/abs\/1412.3555."},{"key":"e_1_3_2_51_2","doi-asserted-by":"publisher","DOI":"10.3390\/s17071528"},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-018-0834-5"},{"key":"e_1_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2950416"},{"key":"e_1_3_2_54_2","article-title":"Dynamic auto-structuring graph neural network: A joint learning framework for origin-destination demand prediction","author":"Dapeng Zhang","year":"2021","unstructured":"Zhang Dapeng and Feng Xiao. 2021. Dynamic auto-structuring graph neural network: A joint learning framework for origin-destination demand prediction. IEEE Transactions on Knowledge and Data Engineering (2021).","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_55_2","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2017.7995699"},{"key":"e_1_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-53967-9"},{"key":"e_1_3_2_57_2","article-title":"Convolutional neural networks on graphs with fast localized spectral filtering","volume":"29","author":"Defferrard Micha\u00ebl","year":"2016","unstructured":"Micha\u00ebl Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. Advances in Neural Information Processing Systems 29 (2016).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_58_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16523"},{"issue":"1","key":"e_1_3_2_59_2","first-page":"1","article-title":"GPS mobility as a digital biomarker of negative symptoms in schizophrenia: A case control study","volume":"2","author":"Depp Colin A.","year":"2019","unstructured":"Colin A. Depp, Jesse Bashem, Raeanne C. Moore, Jason L. Holden, Tanya Mikhael, Joel Swendsen, Philip D. Harvey, and Eric L. Granholm. 2019. GPS mobility as a digital biomarker of negative symptoms in schizophrenia: A case control study. NPJ Digital Medicine 2, 1 (2019), 1\u20137.","journal-title":"NPJ Digital Medicine"},{"key":"e_1_3_2_60_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2021.3100578"},{"key":"e_1_3_2_61_2","unstructured":"Bo Dong Hao Liu Yu Bai Jinbiao Lin Zhuoran Xu Xinyu Xu and Qi Kong. 2021. Multi-modal trajectory prediction for autonomous driving with semantic map and dynamic graph attention network. arXiv:2103.16273. Retrieved from https:\/\/arxiv.org\/abs\/2103.16273."},{"key":"e_1_3_2_62_2","doi-asserted-by":"publisher","DOI":"10.1007\/s41666-021-00098-4"},{"key":"e_1_3_2_63_2","doi-asserted-by":"publisher","DOI":"10.1145\/3450439.3451880"},{"key":"e_1_3_2_64_2","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1109\/CHASE52844.2021.00013","volume-title":"Proceedings of the 2021 IEEE\/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)","author":"Dong Guimin","year":"2021","unstructured":"Guimin Dong, Lihua Cai, Shashwat Kumar, Debajyoti Datta, Laura E. Barnes, and Mehdi Boukhechba. 2021. Detection and analysis of interrupted behaviors by public policy interventions during COVID-19. In Proceedings of the 2021 IEEE\/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE). IEEE, 46\u201357."},{"key":"e_1_3_2_65_2","doi-asserted-by":"crossref","first-page":"1221","DOI":"10.1109\/ICMLA52953.2021.00198","volume-title":"Proceedings of the 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)","author":"Dong Guimin","year":"2021","unstructured":"Guimin Dong, Mingyue Tang, Lihua Cai, Laura E. Barnes, and Mehdi Boukhechba. 2021. Semi-supervised graph instance transformer for mental health inference. In Proceedings of the 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 1221\u20131228."},{"key":"e_1_3_2_66_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2017.2676678"},{"issue":"4","key":"e_1_3_2_67_2","doi-asserted-by":"crossref","first-page":"5026","DOI":"10.1109\/LRA.2020.3004324","article-title":"Probabilistic crowd GAN: Multimodal pedestrian trajectory prediction using a graph vehicle-pedestrian attention network","volume":"5","author":"Eiffert Stuart","year":"2020","unstructured":"Stuart Eiffert, Kunming Li, Mao Shan, Stewart Worrall, Salah Sukkarieh, and Eduardo Nebot. 2020. Probabilistic crowd GAN: Multimodal pedestrian trajectory prediction using a graph vehicle-pedestrian attention network. IEEE Robotics and Automation Letters 5, 4 (2020), 5026\u20135033.","journal-title":"IEEE Robotics and Automation Letters"},{"key":"e_1_3_2_68_2","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313488"},{"issue":"15","key":"e_1_3_2_69_2","doi-asserted-by":"crossref","first-page":"4220","DOI":"10.3390\/s20154220","article-title":"Deep learning sensor fusion for autonomous vehicle perception and localization: A review","volume":"20","author":"Fayyad Jamil","year":"2020","unstructured":"Jamil Fayyad, Mohammad A. Jaradat, Dominique Gruyer, and Homayoun Najjaran. 2020. Deep learning sensor fusion for autonomous vehicle perception and localization: A review. Sensors 20, 15 (2020), 4220.","journal-title":"Sensors"},{"key":"e_1_3_2_70_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3073107"},{"key":"e_1_3_2_71_2","doi-asserted-by":"crossref","DOI":"10.1002\/9780470665121","volume-title":"RFID Handbook: Fundamentals and Applications in Contactless Smart Cards, Radio Frequency Identification and Near-field Communication","author":"Finkenzeller Klaus","year":"2010","unstructured":"Klaus Finkenzeller. 2010. RFID Handbook: Fundamentals and Applications in Contactless Smart Cards, Radio Frequency Identification and Near-field Communication. John wiley & sons."},{"key":"e_1_3_2_72_2","first-page":"313","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Fischer Kai","year":"2021","unstructured":"Kai Fischer, Martin Simon, Florian Olsner, Stefan Milz, Horst-Michael Gross, and Patrick Mader. 2021. Stickypillars: Robust and efficient feature matching on point clouds using graph neural networks. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 313\u2013323."},{"issue":"1","key":"e_1_3_2_73_2","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1109\/TSMCC.2012.2215852","article-title":"Enabling effective programming and flexible management of efficient body sensor network applications","volume":"43","author":"Fortino Giancarlo","year":"2012","unstructured":"Giancarlo Fortino, Roberta Giannantonio, Raffaele Gravina, Philip Kuryloski, and Roozbeh Jafari. 2012. Enabling effective programming and flexible management of efficient body sensor network applications. IEEE Transactions on Human-Machine Systems 43, 1 (2012), 115\u2013133.","journal-title":"IEEE Transactions on Human-Machine Systems"},{"key":"e_1_3_2_74_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-021-03649-2"},{"issue":"51","key":"e_1_3_2_75_2","doi-asserted-by":"crossref","first-page":"14502","DOI":"10.1073\/pnas.1618138113","article-title":"Human\u2013environment interactions in population and ecosystem health","volume":"113","author":"Galvani Alison P.","year":"2016","unstructured":"Alison P. Galvani, Chris T. Bauch, Madhur Anand, Burton H. Singer, and Simon A. Levin. 2016. Human\u2013environment interactions in population and ecosystem health. Proceedings of the National Academy of Sciences 113, 51 (2016), 14502\u201314506.","journal-title":"Proceedings of the National Academy of Sciences"},{"key":"e_1_3_2_76_2","doi-asserted-by":"crossref","first-page":"5260","DOI":"10.1109\/ICASSP39728.2021.9414563","volume-title":"Proceedings of the ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","author":"Gama Fernando","year":"2021","unstructured":"Fernando Gama, Ekaterina Tolstaya, and Alejandro Ribeiro. 2021. Graph neural networks for decentralized controllers. In Proceedings of the ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 5260\u20135264."},{"key":"e_1_3_2_77_2","doi-asserted-by":"publisher","DOI":"10.1109\/CDC45484.2021.9683135"},{"key":"e_1_3_2_78_2","first-page":"769","volume-title":"Proceedings of the 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","author":"Gao Jiechao","year":"2020","unstructured":"Jiechao Gao, Haoyu Wang, and Haiying Shen. 2020. Smartly handling renewable energy instability in supporting a cloud datacenter. In Proceedings of the 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE, 769\u2013778."},{"key":"e_1_3_2_79_2","first-page":"453","volume-title":"Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems","author":"Gao Jiechao","year":"2021","unstructured":"Jiechao Gao, Wenpeng Wang, Zetian Liu, Md Fazlay Rabbi Masum Billah, and Bradford Campbell. 2021. Decentralized federated learning framework for the neighborhood: A case study on residential building load forecasting. In Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems. 453\u2013459."},{"issue":"9","key":"e_1_3_2_80_2","doi-asserted-by":"crossref","first-page":"101150","DOI":"10.1016\/j.apr.2021.101150","article-title":"A graph-based LSTM model for PM2. 5 forecasting","volume":"12","author":"Gao Xi","year":"2021","unstructured":"Xi Gao and Weide Li. 2021. A graph-based LSTM model for PM2. 5 forecasting. Atmospheric Pollution Research 12, 9 (2021), 101150.","journal-title":"Atmospheric Pollution Research"},{"key":"e_1_3_2_81_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.pmcj.2018.09.003"},{"key":"e_1_3_2_82_2","first-page":"1","volume-title":"Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN)","author":"Garcia-Garcia Alberto","year":"2019","unstructured":"Alberto Garcia-Garcia, Brayan S. Zapata-Impata, Sergio Orts-Escolano, Pablo Gil, and Jose Garcia-Rodriguez. 2019. Tactilegcn: A graph convolutional network for predicting grasp stability with tactile sensors. In Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 1\u20138."},{"key":"e_1_3_2_83_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2019.2894665"},{"key":"e_1_3_2_84_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-020-02054-y"},{"key":"e_1_3_2_85_2","first-page":"1263","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Gilmer Justin","year":"2017","unstructured":"Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl. 2017. Neural message passing for quantum chemistry. In Proceedings of the International Conference on Machine Learning. PMLR, 1263\u20131272."},{"key":"e_1_3_2_86_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCBB.2021.3052811"},{"key":"e_1_3_2_87_2","doi-asserted-by":"publisher","DOI":"10.1145\/2818183"},{"key":"e_1_3_2_88_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2012.12.028"},{"key":"e_1_3_2_89_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2020.3039297"},{"key":"e_1_3_2_90_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00240"},{"key":"e_1_3_2_91_2","article-title":"Graph neural network: Current state of Art, challenges and applications","author":"Gupta Atika","year":"2021","unstructured":"Atika Gupta, Priya Matta, and Bhasker Pant. 2021. Graph neural network: Current state of Art, challenges and applications. Materials Today: Proceedings, 46 (2021), 10927\u201310932.","journal-title":"Materials Today: Proceedings"},{"key":"e_1_3_2_92_2","article-title":"Inductive representation learning on large graphs","volume":"30","author":"Hamilton Will","year":"2017","unstructured":"Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. Advances in Neural Information Processing Systems 30 (2017).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_93_2","doi-asserted-by":"publisher","DOI":"10.1145\/3382507.3419747"},{"key":"e_1_3_2_94_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.acha.2010.04.005"},{"key":"e_1_3_2_95_2","first-page":"1","volume-title":"Proceedings of the 2019 IEEE Global Communications Conference (GLOBECOM)","author":"Han Jindong","year":"2019","unstructured":"Jindong Han, Yuan He, Juan Liu, Qianqian Zhang, and Xiaojun Jing. 2019. GraphConvLSTM: Spatiotemporal learning for activity recognition with wearable sensors. In Proceedings of the 2019 IEEE Global Communications Conference (GLOBECOM). IEEE, 1\u20136."},{"key":"e_1_3_2_96_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3174853"},{"key":"e_1_3_2_97_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-020-2669-y"},{"key":"e_1_3_2_98_2","doi-asserted-by":"publisher","DOI":"10.1002\/2014EA000044"},{"key":"e_1_3_2_99_2","doi-asserted-by":"publisher","DOI":"10.3390\/en12173310"},{"key":"e_1_3_2_100_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2020.3008612"},{"key":"e_1_3_2_101_2","first-page":"102","volume-title":"Proceedings of the 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No. 00CH37065)","volume":"1","author":"Hebert Martial","year":"2000","unstructured":"Martial Hebert. 2000. Active and passive range sensing for robotics. In Proceedings of the 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No. 00CH37065), Vol. 1. IEEE, 102\u2013110."},{"key":"e_1_3_2_102_2","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1109\/WF-IoT.2018.8355152","volume-title":"Proceedings of the 2018 IEEE 4th World Forum on Internet of Things (WF-IoT)","author":"Heble Soumil","year":"2018","unstructured":"Soumil Heble, Ajay Kumar, K. V. V. Durga Prasad, Soumya Samirana, Pachamuthu Rajalakshmi, and Uday B. Desai. 2018. A low power IoT network for smart agriculture. In Proceedings of the 2018 IEEE 4th World Forum on Internet of Things (WF-IoT). IEEE, 609\u2013614."},{"key":"e_1_3_2_103_2","first-page":"4900","volume-title":"Proceedings of the ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","author":"Hu Ting-Kuei","year":"2021","unstructured":"Ting-Kuei Hu, Fernando Gama, Tianlong Chen, Zhangyang Wang, Alejandro Ribeiro, and Brian M. Sadler. 2021. VGAI: End-to-end learning of vision-based decentralized controllers for robot swarms. In Proceedings of the ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 4900\u20134904."},{"key":"e_1_3_2_104_2","article-title":"Abnormal event detection using deep contrastive learning for intelligent video surveillance system","author":"Huang Chao","year":"2021","unstructured":"Chao Huang, Zhihao Wu, Jie Wen, Yong Xu, Qiuping Jiang, and Yaowei Wang. 2021. Abnormal event detection using deep contrastive learning for intelligent video surveillance system. IEEE Transactions on Industrial Informatics (2021).","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"e_1_3_2_105_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmsy.2022.05.018"},{"key":"e_1_3_2_106_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.comcom.2009.08.012"},{"issue":"8","key":"e_1_3_2_107_2","doi-asserted-by":"crossref","first-page":"194","DOI":"10.3390\/fi13080194","article-title":"Movement analysis for neurological and musculoskeletal disorders using graph convolutional neural network","volume":"13","author":"Jalata Ibsa K.","year":"2021","unstructured":"Ibsa K. Jalata, Thanh-Dat Truong, Jessica L. Allen, Han-Seok Seo, and Khoa Luu. 2021. Movement analysis for neurological and musculoskeletal disorders using graph convolutional neural network. Future Internet 13, 8 (2021), 194.","journal-title":"Future Internet"},{"key":"e_1_3_2_108_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2018.8462207"},{"key":"e_1_3_2_109_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ssci.2015.12.012"},{"issue":"6","key":"e_1_3_2_110_2","doi-asserted-by":"crossref","first-page":"2463","DOI":"10.1175\/JHM-D-14-0230.1","article-title":"Evaluation of the global land data assimilation system (GLDAS) air temperature data products","volume":"16","author":"Ji Lei","year":"2015","unstructured":"Lei Ji, Gabriel B. Senay, and James P. Verdin. 2015. Evaluation of the global land data assimilation system (GLDAS) air temperature data products. Journal of Hydrometeorology 16, 6 (2015), 2463\u20132480.","journal-title":"Journal of Hydrometeorology"},{"key":"e_1_3_2_111_2","unstructured":"Xiaowei Jia Jacob Zwart Jeffrey Sadler Alison Appling Samantha Oliver Steven Markstrom Jared Willard Shaoming Xu Michael Steinbach Jordan Read and Vipin Kumar. 2020. Physics-guided recurrent graph networks for predicting flow and temperature in river networks. arXiv:2009.12575. Retrieved from https:\/\/arxiv.org\/abs\/2009.12575."},{"key":"e_1_3_2_112_2","first-page":"1324","volume-title":"Proceedings of the 2020 International Joint Conference on Artificial Intelligence","author":"Jia Ziyu","year":"2020","unstructured":"Ziyu Jia, Youfang Lin, Jing Wang, Ronghao Zhou, Xiaojun Ning, Yuanlai He, and Yaoshuai Zhao. 2020. GraphSleepNet: Adaptive spatial-temporal graph convolutional networks for sleep stage classification. In Proceedings of the 2020 International Joint Conference on Artificial Intelligence. 1324\u20131330."},{"key":"e_1_3_2_113_2","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482000"},{"key":"e_1_3_2_114_2","doi-asserted-by":"crossref","unstructured":"Weiwei Jiang and Jiayun Luo. 2021. Graph neural network for traffic forecasting: A survey. Expert Systems with Applications (2022) 117921.","DOI":"10.1016\/j.eswa.2022.117921"},{"key":"e_1_3_2_115_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2939201"},{"key":"e_1_3_2_116_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2021.07.012"},{"issue":"16","key":"e_1_3_2_117_2","doi-asserted-by":"crossref","first-page":"5354","DOI":"10.3390\/s21165354","article-title":"Vehicle trajectory prediction using hierarchical graph neural network for considering interaction among multimodal maneuvers","volume":"21","author":"Jo Eunsan","year":"2021","unstructured":"Eunsan Jo, Myoungho Sunwoo, and Minchul Lee. 2021. Vehicle trajectory prediction using hierarchical graph neural network for considering interaction among multimodal maneuvers. Sensors 21, 16 (2021), 5354.","journal-title":"Sensors"},{"key":"e_1_3_2_118_2","doi-asserted-by":"crossref","unstructured":"Onno Kampman Elham J. Barezi Dario Bertero and Pascale Fung. 2018. Investigating audio visual and text fusion methods for end-to-end automatic personality prediction. arXiv:1805.00705. Retrieved from https:\/\/arxiv.org\/abs\/1805.00705.","DOI":"10.18653\/v1\/P18-2096"},{"key":"e_1_3_2_119_2","volume-title":"Proceedings of the 5th Annual Conference on Robot Learning","author":"Kapelyukh Ivan","year":"2021","unstructured":"Ivan Kapelyukh and Edward Johns. 2021. My house, my rules: Learning tidying preferences with graph neural networks. In Proceedings of the 5th Annual Conference on Robot Learning."},{"key":"e_1_3_2_120_2","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/978-3-030-31620-4_1","volume-title":"Proceedings of the Digital Phenotyping and Mobile Sensing","author":"Kargl Frank","year":"2019","unstructured":"Frank Kargl, Rens W. van der Heijden, Benjamin Erb, and Christoph B\u00f6sch. 2019. Privacy in mobile sensing. In Proceedings of the Digital Phenotyping and Mobile Sensing. Springer, 3\u201312."},{"key":"e_1_3_2_121_2","first-page":"15323","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"35","author":"Karimi Ahmad Maroof","year":"2021","unstructured":"Ahmad Maroof Karimi, Yinghui Wu, Mehmet Koyuturk, and Roger H. French. 2021. Spatiotemporal graph neural network for performance prediction of photovoltaic power systems. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 15323\u201315330."},{"issue":"1","key":"e_1_3_2_122_2","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1109\/TSMC.2020.3018325","article-title":"Deep learning in robotics: Survey on model structures and training strategies","volume":"51","author":"K\u00e1roly Art\u00far Istv\u00e1n","year":"2020","unstructured":"Art\u00far Istv\u00e1n K\u00e1roly, P\u00e9ter Galambos, J\u00f3zsef Kuti, and Imre J. Rudas. 2020. Deep learning in robotics: Survey on model structures and training strategies. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51, 1 (2020), 266\u2013279.","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems"},{"key":"e_1_3_2_123_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3058219"},{"key":"e_1_3_2_124_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2015.10.013"},{"issue":"3","key":"e_1_3_2_125_2","article-title":"Large scale distributed collaborative unlabeled motion planning with graph policy gradients","volume":"6","author":"Khan Arbaaz","year":"2020","unstructured":"Arbaaz Khan, Vijay Kumar, and Alejandro Ribeiro. 2020. Large scale distributed collaborative unlabeled motion planning with graph policy gradients. IEEE Robotics and Automation Letters 6, 3 (2020), 5340\u20135347.","journal-title":"IEEE Robotics and Automation Letters"},{"key":"e_1_3_2_126_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.sbsr.2020.100337"},{"key":"e_1_3_2_127_2","doi-asserted-by":"publisher","DOI":"10.2200\/S00822ED1V01Y201712COV015"},{"key":"e_1_3_2_128_2","doi-asserted-by":"publisher","DOI":"10.3390\/jsan8030045"},{"key":"e_1_3_2_129_2","unstructured":"J. Taery Kim Jeongeun Park Sungjoon Choi and Sehoon Ha. 2021. Learning robot structure and motion embeddings using graph neural networks. arXiv:2109.07543. Retrieved from https:\/\/arxiv.org\/abs\/2109.07543."},{"key":"e_1_3_2_130_2","unstructured":"Thomas N. Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. International Conference on Learning Representations ."},{"key":"e_1_3_2_131_2","unstructured":"Thomas N. Kipf and Max Welling. 2016. Variational graph auto-encoders. arXiv:1611.07308. Retrieved from https:\/\/arxiv.org\/abs\/1611.07308."},{"key":"e_1_3_2_132_2","first-page":"137","article-title":"Social-BiGAT: Multimodal trajectory forecasting using Bicycle-GAN and graph attention networks","volume":"32","author":"Kosaraju Vineet","year":"2019","unstructured":"Vineet Kosaraju, Amir Sadeghian, Roberto Mart\u00edn-Mart\u00edn, Ian Reid, Hamid Rezatofighi, and Silvio Savarese. 2019. Social-BiGAT: Multimodal trajectory forecasting using Bicycle-GAN and graph attention networks. Advances in Neural Information Processing Systems 32 (2019), 137\u2013146.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_133_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2014.2356651"},{"key":"e_1_3_2_134_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2011.2148171"},{"key":"e_1_3_2_135_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2962338"},{"key":"e_1_3_2_136_2","first-page":"1","volume-title":"Proceedings of the 2020 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia)","author":"Kwak Youngchul","year":"2020","unstructured":"Youngchul Kwak, Woo-Jin Song, and Seong-Eun Kim. 2020. Graph neural network with multilevel feature fusion for EEG based brain-computer interface. In Proceedings of the 2020 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia). IEEE, 1\u20133."},{"key":"e_1_3_2_137_2","doi-asserted-by":"publisher","DOI":"10.1145\/1964897.1964918"},{"key":"e_1_3_2_138_2","first-page":"1","volume-title":"Proceedings of the 2016 15th ACM\/IEEE International Conference on Information Processing in Sensor Networks (IPSN)","author":"Lane Nicholas D.","year":"2016","unstructured":"Nicholas D. Lane, Sourav Bhattacharya, Petko Georgiev, Claudio Forlivesi, Lei Jiao, Lorena Qendro, and Fahim Kawsar. 2016. Deepx: A software accelerator for low-power deep learning inference on mobile devices. In Proceedings of the 2016 15th ACM\/IEEE International Conference on Information Processing in Sensor Networks (IPSN). IEEE, 1\u201312."},{"key":"e_1_3_2_139_2","doi-asserted-by":"crossref","first-page":"8484","DOI":"10.1109\/ICASSP40776.2020.9053986","volume-title":"Proceedings of the ICASSP 2020\u20132020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","author":"Lassance Carlos","year":"2020","unstructured":"Carlos Lassance, Myriam Bontonou, Ghouthi Boukli Hacene, Vincent Gripon, Jian Tang, and Antonio Ortega. 2020. Deep geometric knowledge distillation with graphs. In Proceedings of the ICASSP 2020\u20132020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 8484\u20138488."},{"key":"e_1_3_2_140_2","unstructured":"Donsuk Lee Yiming Gu Jerrick Hoang and Micol Marchetti-Bowick. 2019. Joint interaction and trajectory prediction for autonomous driving using graph neural networks. arXiv:1912.07882. Retrieved from https:\/\/arxiv.org\/abs\/1912.07882."},{"key":"e_1_3_2_141_2","doi-asserted-by":"publisher","DOI":"10.1177\/1550147716665500"},{"key":"e_1_3_2_142_2","unstructured":"Seunghyun Lee and Byung Cheol Song. 2019. Graph-based knowledge distillation by multi-head attention network. arXiv:1907.02226. Retrieved from https:\/\/arxiv.org\/abs\/1907.02226."},{"key":"e_1_3_2_143_2","doi-asserted-by":"crossref","unstructured":"Kun Lei Peng Guo Yi Wang Xiao Wu and Wenchao Zhao. 2021. Solve routing problems with a residual edge-graph attention neural network. Neurocomputing 508 (2021) 79\u201398.","DOI":"10.1016\/j.neucom.2022.08.005"},{"key":"e_1_3_2_144_2","article-title":"PEN: Process estimator neural network for root cause analysis using graph convolution","author":"Leonhardt Viktor","year":"2021","unstructured":"Viktor Leonhardt, Felix Claus, and Christoph Garth. 2021. PEN: Process estimator neural network for root cause analysis using graph convolution. Journal of Manufacturing Systems 62 (2022), 886\u2013902.","journal-title":"Journal of Manufacturing Systems"},{"key":"e_1_3_2_145_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2020.3041234"},{"key":"e_1_3_2_146_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2021.3082868"},{"key":"e_1_3_2_147_2","first-page":"689","article-title":"Cross-subject EEG emotion recognition with self-organized graph neural network","volume":"15","author":"Li Jingcong","year":"2021","unstructured":"Jingcong Li, Shuqi Li, Jiahui Pan, and Fei Wang. 2021. Cross-subject EEG emotion recognition with self-organized graph neural network. Frontiers in Neuroscience 15 (2021), 689.","journal-title":"Frontiers in Neuroscience"},{"key":"e_1_3_2_148_2","unstructured":"Jiachen Li Hengbo Ma Zhihao Zhang and Masayoshi Tomizuka. 2020. Social-wagdat: Interaction-aware trajectory prediction via wasserstein graph double-attention network. arXiv:2002.06241. Retrieved from https:\/\/arxiv.org\/abs\/2002.06241."},{"key":"e_1_3_2_149_2","unstructured":"Kunming Li Stuart Eiffert Mao Shan Francisco Gomez-Donoso Stewart Worrall and Eduardo Nebot. 2020. Attentional-GCNN: Adaptive pedestrian trajectory prediction towards generic autonomous vehicle use cases. arXiv:2011.11190. Retrieved from https:\/\/arxiv.org\/abs\/2011.11190."},{"key":"e_1_3_2_150_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA48506.2021.9561480"},{"key":"e_1_3_2_151_2","first-page":"344","volume-title":"Proceedings of the 2017 14th International Symposium on Pervasive Systems, Algorithms and Networks & 2017 11th International Conference on Frontier of Computer Science and Technology & 2017 Third International Symposium of Creative Computing (ISPAN-FCST-ISCC)","author":"Li Liangzhi","year":"2017","unstructured":"Liangzhi Li, Kaoru Ota, and Mianxiong Dong. 2017. Everything is image: CNN-based short-term electrical load forecasting for smart grid. In Proceedings of the 2017 14th International Symposium on Pervasive Systems, Algorithms and Networks & 2017 11th International Conference on Frontier of Computer Science and Technology & 2017 Third International Symposium of Creative Computing (ISPAN-FCST-ISCC). IEEE, 344\u2013351."},{"key":"e_1_3_2_152_2","first-page":"214","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Li Maosen","year":"2020","unstructured":"Maosen Li, Siheng Chen, Yangheng Zhao, Ya Zhang, Yanfeng Wang, and Qi Tian. 2020. Dynamic multiscale graph neural networks for 3d skeleton based human motion prediction. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 214\u2013223."},{"key":"e_1_3_2_153_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3108708"},{"issue":"3","key":"e_1_3_2_154_2","doi-asserted-by":"crossref","first-page":"5533","DOI":"10.1109\/LRA.2021.3077863","article-title":"Message-aware graph attention networks for large-scale multi-robot path planning","volume":"6","author":"Li Qingbiao","year":"2021","unstructured":"Qingbiao Li, Weizhe Lin, Zhe Liu, and Amanda Prorok. 2021. Message-aware graph attention networks for large-scale multi-robot path planning. IEEE Robotics and Automation Letters 6, 3 (2021), 5533\u20135540.","journal-title":"IEEE Robotics and Automation Letters"},{"key":"e_1_3_2_155_2","doi-asserted-by":"publisher","DOI":"10.1109\/RBME.2021.3122522"},{"key":"e_1_3_2_156_2","doi-asserted-by":"publisher","DOI":"10.1145\/3460199"},{"key":"e_1_3_2_157_2","first-page":"389","volume-title":"Proceedings of the 2019 IEEE International Conference on Data Mining (ICDM)","author":"Li Xiaoyu","year":"2019","unstructured":"Xiaoyu Li, Buyue Qian, Jishang Wei, An Li, Xuan Liu, and Qinghua Zheng. 2019. Classify EEG and reveal latent graph structure with spatio-temporal graph convolutional neural network. In Proceedings of the 2019 IEEE International Conference on Data Mining (ICDM). IEEE, 389\u2013398."},{"key":"e_1_3_2_158_2","first-page":"352","volume-title":"Proceedings of the 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","author":"Li Xiang","year":"2016","unstructured":"Xiang Li, Dawei Song, Peng Zhang, Guangliang Yu, Yuexian Hou, and Bin Hu. 2016. Emotion recognition from multi-channel EEG data through convolutional recurrent neural network. In Proceedings of the 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 352\u2013359."},{"issue":"23","key":"e_1_3_2_159_2","doi-asserted-by":"crossref","first-page":"4003","DOI":"10.3390\/rs12234003","article-title":"Multi-label remote sensing image scene classification by combining a convolutional neural network and a graph neural network","volume":"12","author":"Li Yansheng","year":"2020","unstructured":"Yansheng Li, Ruixian Chen, Yongjun Zhang, Mi Zhang, and Ling Chen. 2020. Multi-label remote sensing image scene classification by combining a convolutional neural network and a graph neural network. Remote Sensing 12, 23 (2020), 4003.","journal-title":"Remote Sensing"},{"key":"e_1_3_2_160_2","unstructured":"Yujia Li Daniel Tarlow Marc Brockschmidt and Richard Zemel. 2016. Gated graph sequence neural networks. In Proceedings of (ICLR\u201916) ."},{"key":"e_1_3_2_161_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMECH.2021.3073736"},{"key":"e_1_3_2_162_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2020.3011333"},{"key":"e_1_3_2_163_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.3041461"},{"key":"e_1_3_2_164_2","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2022.3143518"},{"key":"e_1_3_2_165_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.proenv.2011.12.088"},{"key":"e_1_3_2_166_2","unstructured":"Changchang Liu Supriyo Chakraborty and Prateek Mittal. 2017. Deeprotect: Enabling inference-based access control on mobile sensing applications. arXiv:1702.06159. Retrieved from https:\/\/arxiv.org\/abs\/1702.06159."},{"key":"e_1_3_2_167_2","first-page":"466","volume-title":"Proceedings of the International Conference on Artificial Intelligence and Soft Computing","author":"Liu Chun","year":"2012","unstructured":"Chun Liu and Andreas Kroll. 2012. A centralized multi-robot task allocation for industrial plant inspection by using a* and genetic algorithms. In Proceedings of the International Conference on Artificial Intelligence and Soft Computing. Springer, 466\u2013474."},{"key":"e_1_3_2_168_2","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939776"},{"key":"e_1_3_2_169_2","unstructured":"Kuan Liu Yanen Li Ning Xu and Prem Natarajan. 2018. Learn to combine modalities in multimodal deep learning. arXiv:1805.11730. Retrieved from https:\/\/arxiv.org\/abs\/1805.11730."},{"key":"e_1_3_2_170_2","first-page":"1922","volume-title":"Proceedings of the European Conference on Artificial Intelligence","author":"Liu Ruiqiang","year":"2020","unstructured":"Ruiqiang Liu, Shuai Zhao, Bo Cheng, Hao Yang, Haina Tang, and Fangfang Yang. 2020. ST-MFM: A spatiotemporal multi-modal fusion model for urban anomalies prediction. In Proceedings of the European Conference on Artificial Intelligence. IOS Press, 1922\u20131929."},{"issue":"3","key":"e_1_3_2_171_2","first-page":"1","article-title":"Handling missing sensors in topology-aware iot applications with gated graph neural network","volume":"4","year":"2020","unstructured":"Shengzhong Liu, Shuochao Yao, Yifei Huang, Dongxin Liu, Huajie Shao, Yiran Zhao, Jinyang Li, Tianshi Wang, Ruijie Wang, Chaoqi Yang, and T. Abdelzaher. 2020. Handling missing sensors in topology-aware iot applications with gated graph neural network. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 4, 3 (2020), 1\u201331.","journal-title":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"},{"issue":"10","key":"e_1_3_2_172_2","doi-asserted-by":"crossref","first-page":"101197","DOI":"10.1016\/j.apr.2021.101197","article-title":"A new multi-data-driven spatiotemporal PM2. 5 forecasting model based on an ensemble graph reinforcement learning convolutional network","volume":"12","author":"Liu Xinwei","year":"2021","unstructured":"Xinwei Liu, Muchuan Qin, Yue He, Xiwei Mi, and Chengqing Yu. 2021. A new multi-data-driven spatiotemporal PM2. 5 forecasting model based on an ensemble graph reinforcement learning convolutional network. Atmospheric Pollution Research 12, 10 (2021), 101197.","journal-title":"Atmospheric Pollution Research"},{"key":"e_1_3_2_173_2","first-page":"7096","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Liu Yufan","year":"2019","unstructured":"Yufan Liu, Jiajiong Cao, Bing Li, Chunfeng Yuan, Weiming Hu, Yangxi Li, and Yunqiang Duan. 2019. Knowledge distillation via instance relationship graph. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 7096\u20137104."},{"key":"e_1_3_2_174_2","doi-asserted-by":"publisher","DOI":"10.1080\/00045608.2015.1018773"},{"key":"e_1_3_2_175_2","first-page":"1196","volume-title":"Proceedings of the 2020 IEEE International Conference on Big Data (Big Data)","author":"Liu Zheng","year":"2020","unstructured":"Zheng Liu, Xiaohan Li, Hao Peng, Lifang He, and S. Yu Philip. 2020. Heterogeneous similarity graph neural network on electronic health records. In Proceedings of the 2020 IEEE International Conference on Big Data (Big Data). IEEE, 1196\u20131205."},{"key":"e_1_3_2_176_2","first-page":"467","volume-title":"Proceedings of the International Conference of Transportation Professionals","author":"Liu Zhanghui","year":"2021","unstructured":"Zhanghui Liu and Huachun Tan. 2021. Traffic prediction with graph neural network: A survey. In Proceedings of the International Conference of Transportation Professionals. 467\u2013474."},{"key":"e_1_3_2_177_2","doi-asserted-by":"publisher","DOI":"10.1109\/NOMS54207.2022.9789878"},{"key":"e_1_3_2_178_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3202569"},{"key":"e_1_3_2_179_2","first-page":"3268","volume-title":"Proceedings of the 2011 IEEE\/RSJ International Conference on Intelligent Robots and Systems","author":"Luna Ryan","year":"2011","unstructured":"Ryan Luna and Kostas E. Bekris. 2011. Efficient and complete centralized multi-robot path planning. In Proceedings of the 2011 IEEE\/RSJ International Conference on Intelligent Robots and Systems. IEEE, 3268\u20133275."},{"key":"e_1_3_2_180_2","first-page":"1","article-title":"Computer vision-based descriptive analytics of seniors\u2019 daily activities for long-term health monitoring","volume":"2","year":"2018","unstructured":"Zelun Luo, Jun-Ting, Niranjan Balachandar, Serena Yeung, Guido Pusiol, Jay Luxenberg, Grace Li, Li-Jia Li, N. Lance Downing, Arnold Milstein, and Li Fei-Fei. 2018. Computer vision-based descriptive analytics of seniors\u2019 daily activities for long-term health monitoring. Machine Learning for Healthcare (MLHC) 2 (2018), 1.","journal-title":"Machine Learning for Healthcare (MLHC)"},{"issue":"4","key":"e_1_3_2_181_2","doi-asserted-by":"crossref","first-page":"630","DOI":"10.1097\/CCM.0000000000002265","article-title":"Measuring patient mobility in the ICU using a novel noninvasive sensor","volume":"45","author":"Ma Andy J.","year":"2017","unstructured":"Andy J. Ma, Nishi Rawat, Austin Reiter, Christine Shrock, Andong Zhan, Alex Stone, Anahita Rabiee, Stephanie Griffin, Dale M. Needham, and Suchi Saria. 2017. Measuring patient mobility in the ICU using a novel noninvasive sensor. Critical Care Medicine 45, 4 (2017), 630.","journal-title":"Critical Care Medicine"},{"key":"e_1_3_2_182_2","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2021.3104334"},{"key":"e_1_3_2_183_2","doi-asserted-by":"publisher","DOI":"10.1017\/9781108924184"},{"key":"e_1_3_2_184_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.techfore.2018.08.015"},{"key":"e_1_3_2_185_2","unstructured":"A. Masciadri S. Brusadella A. Tocchetti S. Comai and F. Salice. 2019. Detecting social interaction in a smart environment. Technology and Disability 31 (2019) 119\u201320."},{"key":"e_1_3_2_186_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11370-019-00302-w"},{"key":"e_1_3_2_187_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICMDCS.2017.8211551"},{"key":"e_1_3_2_188_2","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2019.00325.10.3389\/fneur.2019.00325"},{"key":"e_1_3_2_189_2","doi-asserted-by":"publisher","DOI":"10.1109\/tnn.2008.2010350"},{"key":"e_1_3_2_190_2","unstructured":"Rahul Mishra Hari Prabhat Gupta and Tanima Dutta. 2020. A survey on deep neural network compression: Challenges overview and solutions. arXiv:2010.03954. Retrieved from https:\/\/arxiv.org\/abs\/2010.03954."},{"key":"e_1_3_2_191_2","unstructured":"Xiaoyu Mo Yang Xing and Chen Lv. 2020. ReCoG: A deep learning framework with heterogeneous graph for interaction-aware trajectory prediction. arXiv:2012.05032. Retrieved from https:\/\/arxiv.org\/abs\/2012.05032."},{"key":"e_1_3_2_192_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01443"},{"key":"e_1_3_2_193_2","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2018.2844341"},{"issue":"10","key":"e_1_3_2_194_2","doi-asserted-by":"crossref","first-page":"11461","DOI":"10.1109\/JSEN.2020.3015726","article-title":"A new framework for smartphone sensor-based human activity recognition using graph neural network","volume":"21","author":"Mondal Riktim","year":"2020","unstructured":"Riktim Mondal, Debadyuti Mukherjee, Pawan Kumar Singh, Vikrant Bhateja, and Ram Sarkar. 2020. A new framework for smartphone sensor-based human activity recognition using graph neural network. IEEE Sensors Journal 21, 10 (2020), 11461\u201311468.","journal-title":"IEEE Sensors Journal"},{"key":"e_1_3_2_195_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.576"},{"issue":"3","key":"e_1_3_2_196_2","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1007\/s42486-020-00037-z","article-title":"IoT reliability: A review leading to 5 key research directions","volume":"2","author":"Moore Samuel J.","year":"2020","unstructured":"Samuel J. Moore, Chris D. Nugent, Shuai Zhang, and Ian Cleland. 2020. IoT reliability: A review leading to 5 key research directions. CCF Transactions on Pervasive Computing and Interaction 2, 3 (2020), 147\u2013163.","journal-title":"CCF Transactions on Pervasive Computing and Interaction"},{"key":"e_1_3_2_197_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2013.2262376"},{"key":"e_1_3_2_198_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33014602"},{"key":"e_1_3_2_199_2","volume-title":"Marx, Durkheim, Weber: Formations of modern social thought","author":"Morrison Ken","year":"2006","unstructured":"Ken Morrison. 2006. Marx, Durkheim, Weber: Formations of modern social thought. Sage."},{"issue":"4","key":"e_1_3_2_200_2","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1109\/TETC.2016.2606384","article-title":"A comprehensive study of security of internet-of-things","volume":"5","author":"Mosenia Arsalan","year":"2016","unstructured":"Arsalan Mosenia and Niraj K. Jha. 2016. A comprehensive study of security of internet-of-things. IEEE Transactions on Emerging Topics in Computing 5, 4 (2016), 586\u2013602.","journal-title":"IEEE Transactions on Emerging Topics in Computing"},{"key":"e_1_3_2_201_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.addma.2021.102449"},{"key":"e_1_3_2_202_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.pmcj.2018.07.004"},{"key":"e_1_3_2_203_2","unstructured":"Jyoti Narwariya Pankaj Malhotra Vishnu TV Lovekesh Vig and Gautam Shroff. 2020. Graph neural networks for leveraging industrial equipment structure: An application to remaining useful life estimation. arXiv:2006.16556. Retrieved from https:\/\/arxiv.org\/abs\/2006.16556."},{"key":"e_1_3_2_204_2","unstructured":"Usman Nazir He Wang and Murtaza Taj. 2021. Survey of image based graph neural networks. arXiv:2106.06307. Retrieved from https:\/\/arxiv.org\/abs\/2106.06307."},{"issue":"5","key":"e_1_3_2_205_2","doi-asserted-by":"crossref","first-page":"764","DOI":"10.3390\/electronics9050764","article-title":"Facial landmark-based emotion recognition via directed graph neural network","volume":"9","author":"Ngoc Quang Tran","year":"2020","unstructured":"Quang Tran Ngoc, Seunghyun Lee, and Byung Cheol Song. 2020. Facial landmark-based emotion recognition via directed graph neural network. Electronics 9, 5 (2020), 764.","journal-title":"Electronics"},{"key":"e_1_3_2_206_2","first-page":"64","volume-title":"Proceedings of the Australasian Conference on Data Mining","author":"Nguyen Hung","year":"2019","unstructured":"Hung Nguyen, Duc Thanh Nguyen, and Thin Nguyen. 2019. Estimating county health indices using graph neural networks. In Proceedings of the Australasian Conference on Data Mining. Springer, 64\u201376."},{"issue":"6","key":"e_1_3_2_207_2","doi-asserted-by":"crossref","first-page":"628","DOI":"10.1111\/jsr.12169","article-title":"Montreal archive of sleep studies: An open-access resource for instrument benchmarking and exploratory research","volume":"23","author":"O\u2019reilly Christian","year":"2014","unstructured":"Christian O\u2019reilly, Nadia Gosselin, Julie Carrier, and Tore Nielsen. 2014. Montreal archive of sleep studies: An open-access resource for instrument benchmarking and exploratory research. Journal of Sleep Research 23, 6 (2014), 628\u2013635.","journal-title":"Journal of Sleep Research"},{"key":"e_1_3_2_208_2","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)WR.1943-5452.0000191"},{"key":"e_1_3_2_209_2","doi-asserted-by":"publisher","DOI":"10.3390\/rs13010119"},{"key":"e_1_3_2_210_2","first-page":"1","volume-title":"Proceedings of the 2021 International Joint Conference on Neural Networks (IJCNN)","author":"Ouyang Xiaocao","year":"2021","unstructured":"Xiaocao Ouyang, Yan Yang, Yiling Zhang, and Wei Zhou. 2021. Spatial-temporal dynamic graph convolution neural network for air quality prediction. In Proceedings of the 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 1\u20138."},{"key":"e_1_3_2_211_2","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1109\/GlobalSIP.2018.8646486","volume-title":"Proceedings of the 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","author":"Owerko Damian","year":"2018","unstructured":"Damian Owerko, Fernando Gama, and Alejandro Ribeiro. 2018. Predicting power outages using graph neural networks. In Proceedings of the 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 743\u2013747."},{"key":"e_1_3_2_212_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-021-02293-7"},{"key":"e_1_3_2_213_2","doi-asserted-by":"crossref","unstructured":"Shirui Pan Ruiqi Hu Guodong Long Jing Jiang Lina Yao and Chengqi Zhang. 2018. Adversarially regularized graph autoencoder for graph embedding. In Proceedings of the 27th International Joint Conference on Artificial Intelligence . 2609\u20132615.","DOI":"10.24963\/ijcai.2018\/362"},{"key":"e_1_3_2_214_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2021.3049714"},{"key":"e_1_3_2_215_2","first-page":"1080","volume-title":"Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision","year":"2020","unstructured":"Behnoosh Parsa, Athma Narayanan, and Behzad Dariush. 2020. Spatio-temporal pyramid graph convolutions for human action recognition and postural assessment. In Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision. 1080\u20131090."},{"issue":"28","key":"e_1_3_2_216_2","doi-asserted-by":"crossref","first-page":"eaau8479","DOI":"10.1126\/scirobotics.aau8479","article-title":"A review of collective robotic construction","volume":"4","author":"Petersen Kirstin H.","year":"2019","unstructured":"Kirstin H. Petersen, Nils Napp, Robert Stuart-Smith, Daniela Rus, and Mirko Kovac. 2019. A review of collective robotic construction. Science Robotics 4, 28 (2019), eaau8479.","journal-title":"Science Robotics"},{"issue":"12","key":"e_1_3_2_217_2","doi-asserted-by":"crossref","first-page":"5037","DOI":"10.1109\/JSEN.2016.2555935","article-title":"Air quality monitoring system based on ISO\/IEC\/IEEE 21451 standards","volume":"16","author":"Phala Kgoputjo Simon Elvis","year":"2016","unstructured":"Kgoputjo Simon Elvis Phala, Anuj Kumar, and Gerhard P. Hancke. 2016. Air quality monitoring system based on ISO\/IEC\/IEEE 21451 standards. IEEE Sensors Journal 16, 12 (2016), 5037\u20135045.","journal-title":"IEEE Sensors Journal"},{"key":"e_1_3_2_218_2","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1109\/ISBB.2015.7344944","volume-title":"Proceedings of the 2015 International Symposium on Bioelectronics and Bioinformatics (ISBB)","author":"Phan Dung","year":"2015","unstructured":"Dung Phan, Lee Yee Siong, Pubudu N. Pathirana, and Aruna Seneviratne. 2015. Smartwatch: Performance evaluation for long-term heart rate monitoring. In Proceedings of the 2015 International Symposium on Bioelectronics and Bioinformatics (ISBB). IEEE, 144\u2013147."},{"key":"e_1_3_2_219_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2019.01.333"},{"key":"e_1_3_2_220_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2926642"},{"key":"e_1_3_2_221_2","unstructured":"Akshay Rangesh Pranav Maheshwari Mez Gebre Siddhesh Mhatre Vahid Ramezani and Mohan M. Trivedi. 2021. TrackMPNN: A message passing graph neural architecture for multi-object tracking. arXiv:2101.04206. Retrieved from https:\/\/arxiv.org\/abs\/2101.04206."},{"key":"e_1_3_2_222_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2014.2335151"},{"key":"e_1_3_2_223_2","volume-title":"Description of the National Hydrologic Model for Use with the Precipitation-Runoff Modeling System (prms)","author":"Regan R. Steven","year":"2018","unstructured":"R. Steven Regan, Steven L. Markstrom, Lauren E. Hay, Roland J. Viger, Parker A. Norton, Jessica M. Driscoll, and Jacob H. LaFontaine. 2018. Description of the National Hydrologic Model for Use with the Precipitation-Runoff Modeling System (prms). Technical Report. US Geological Survey."},{"key":"e_1_3_2_224_2","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1109\/ISWC.2012.13","volume-title":"Proceedings of the 2012 16th International Symposium on Wearable Computers","author":"Reiss Attila","year":"2012","unstructured":"Attila Reiss and Didier Stricker. 2012. Introducing a new benchmarked dataset for activity monitoring. In Proceedings of the 2012 16th International Symposium on Wearable Computers. IEEE, 108\u2013109."},{"key":"e_1_3_2_225_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46484-8_33"},{"key":"e_1_3_2_226_2","doi-asserted-by":"crossref","unstructured":"Kezhen Rong Minglei Fu Jiawei Chen Lejin Zheng Jianfeng Zheng and Zaher Mundher Yaseen. 2021. Graph neural network for integrated water network partitioning and dynamic district metered areas. Research Square (2021).","DOI":"10.21203\/rs.3.rs-772506\/v1"},{"key":"e_1_3_2_227_2","first-page":"7236","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"34","author":"Ryu Heechang","year":"2020","unstructured":"Heechang Ryu, Hayong Shin, and Jinkyoo Park. 2020. Multi-agent actor-critic with hierarchical graph attention network. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 7236\u20137243."},{"key":"e_1_3_2_228_2","doi-asserted-by":"publisher","DOI":"10.1145\/3161587.3161591"},{"key":"e_1_3_2_229_2","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1007\/978-3-030-35291-2_2","volume-title":"Proceedings of the Internet of Things for Sustainable Community Development","author":"Salam Abdul","year":"2020","unstructured":"Abdul Salam. 2020. Internet of things for environmental sustainability and climate change. In Proceedings of the Internet of Things for Sustainable Community Development. Springer, 33\u201369."},{"key":"e_1_3_2_230_2","doi-asserted-by":"crossref","first-page":"1551","DOI":"10.1109\/SMC.2019.8914537","volume-title":"Proceedings of the 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)","author":"Salekin Md Sirajus","year":"2019","unstructured":"Md Sirajus Salekin, Ghada Zamzmi, Dmitry Goldgof, Rangachar Kasturi, Thao Ho, and Yu Sun. 2019. Multi-channel neural network for assessing neonatal pain from videos. In Proceedings of the 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC). IEEE, 1551\u20131556."},{"key":"e_1_3_2_231_2","unstructured":"Ryoma Sato. 2020. A survey on the expressive power of graph neural networks. arXiv:2003.04078. Retrieved from https:\/\/arxiv.org\/abs\/2003.04078."},{"key":"e_1_3_2_232_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2008.2005605"},{"key":"e_1_3_2_233_2","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC55140.2022.9922205"},{"key":"e_1_3_2_234_2","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2017.2731979"},{"issue":"2","key":"e_1_3_2_235_2","article-title":"Spatiotemporal prediction of air quality based on LSTM neural network","volume":"60","author":"Seng Dewen","year":"2021","unstructured":"Dewen Seng, Qiyan Zhang, Xuefeng Zhang, Guangsen Chen, and Xiyuan Chen. 2021. Spatiotemporal prediction of air quality based on LSTM neural network. Alexandria Engineering Journal 60, 2 (2021), 2021\u20132032.","journal-title":"Alexandria Engineering Journal"},{"key":"e_1_3_2_236_2","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1109\/ISVLSI.2017.124","volume-title":"Proceedings of the 2017 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)","author":"Shafique Muhammad","year":"2017","unstructured":"Muhammad Shafique, Rehan Hafiz, Muhammad Usama Javed, Sarmad Abbas, Lukas Sekanina, Zdenek Vasicek, and Vojtech Mrazek. 2017. Adaptive and energy-efficient architectures for machine learning: Challenges, opportunities, and research roadmap. In Proceedings of the 2017 IEEE Computer Society Annual Symposium on VLSI (ISVLSI). IEEE, 627\u2013632."},{"key":"e_1_3_2_237_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.115"},{"key":"e_1_3_2_238_2","first-page":"906","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"34","author":"Shi Han","year":"2020","unstructured":"Han Shi, Haozheng Fan, and James T. Kwok. 2020. Effective decoding in graph auto-encoder using triadic closure. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 906\u2013913."},{"key":"e_1_3_2_239_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00810"},{"key":"e_1_3_2_240_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00178"},{"key":"e_1_3_2_241_2","first-page":"564","volume-title":"Proceedings of the International Conference in Swarm Intelligence","author":"Shi Zhiguo","year":"2012","unstructured":"Zhiguo Shi, Jun Tu, Qiao Zhang, Lei Liu, and Junming Wei. 2012. A survey of swarm robotics system. In Proceedings of the International Conference in Swarm Intelligence. Springer, 564\u2013572."},{"key":"e_1_3_2_242_2","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1007\/978-3-030-77214-7_9","volume-title":"Proceedings of the Smart Sensor Networks","author":"Shrivastava Namita","year":"2022","unstructured":"Namita Shrivastava, Amit Bhagat, and Rajit Nair. 2022. Graph powered machine learning in smart sensor networks. In Proceedings of the Smart Sensor Networks. Springer, 209\u2013226."},{"key":"e_1_3_2_243_2","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2012.2235192"},{"key":"e_1_3_2_244_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00132"},{"key":"e_1_3_2_245_2","first-page":"1","volume-title":"Proceedings of the 2016 International Conference on Emerging Trends in Communication Technologies (ETCT)","author":"Singh Abhishek","year":"2016","unstructured":"Abhishek Singh, Nagesh B. Balam, Anuj Kumar, and Ashok Kumar. 2016. An intelligent color sensing system for building wall. In Proceedings of the 2016 International Conference on Emerging Trends in Communication Technologies (ETCT). IEEE, 1\u20134."},{"key":"e_1_3_2_246_2","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1109\/INFCOMW.2017.8116438","volume-title":"Proceedings of the 2017 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)","author":"Sivanathan Arunan","year":"2017","unstructured":"Arunan Sivanathan, Daniel Sherratt, Hassan Habibi Gharakheili, Adam Radford, Chamith Wijenayake, Arun Vishwanath, and Vijay Sivaraman. 2017. Characterizing and classifying IoT traffic in smart cities and campuses. In Proceedings of the 2017 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE, 559\u2013564."},{"key":"e_1_3_2_247_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3082932"},{"key":"e_1_3_2_248_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2020.110591"},{"key":"e_1_3_2_249_2","doi-asserted-by":"publisher","DOI":"10.1109\/TAFFC.2018.2817622"},{"key":"e_1_3_2_250_2","doi-asserted-by":"crossref","first-page":"596","DOI":"10.1109\/ICNIDC.2012.6418824","volume-title":"Proceedings of the 2012 3rd IEEE International Conference on Network Infrastructure and Digital Content","author":"Song Yang","year":"2012","unstructured":"Yang Song, Bingjun Han, Xin Zhang, and Dacheng Yang. 2012. Modeling and simulation of smart home scenarios based on Internet of Things. In Proceedings of the 2012 3rd IEEE International Conference on Network Infrastructure and Digital Content. IEEE, 596\u2013600."},{"key":"e_1_3_2_251_2","doi-asserted-by":"publisher","DOI":"10.1098\/rsta.2011.0244"},{"key":"e_1_3_2_252_2","doi-asserted-by":"publisher","DOI":"10.1145\/2809695.2809718"},{"issue":"4","key":"e_1_3_2_253_2","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1038\/s41591-021-01292-y","article-title":"Attributes and predictors of long COVID","volume":"27","year":"2021","unstructured":"Carole H. Sudre, Benjamin Murray, Thomas Varsavsky, Mark S. Graham, Rose S. Penfold, Ruth C. Bowyer, Joan Capdevila Pujol, Kerstin Klaser, Michela Antonelli, Liane S. Canas, Erika Molteni, Marc Modat, M. Jorge Cardoso, Anna May, Sajaysurya Ganesh, Richard Davies, Long H. Nguyen, David A. Drew, Christina M. Astley, Amit D. Joshi, Jordi Merino, Neli Tsereteli, Tove Fall, Maria F. Gomez, Emma L. Duncan, Cristina Menni, Frances M. K. Williams, Paul W. Franks, Andrew T. Chan, Jonathan Wolf, Sebastien Ourselin, Tim Spector, and Claire J. Steves. 2021. Attributes and predictors of long COVID. Nature Medicine 27, 4 (2021), 626\u2013631.","journal-title":"Nature Medicine"},{"issue":"12","key":"e_1_3_2_254_2","doi-asserted-by":"crossref","first-page":"e2021WR030394","DOI":"10.1029\/2021WR030394","article-title":"Explore spatio-temporal learning of large sample hydrology using graph neural networks","volume":"57","author":"Sun Alexander Y.","year":"2021","unstructured":"Alexander Y. Sun, Peishi Jiang, Maruti K. Mudunuru, and Xingyuan Chen. 2021. Explore spatio-temporal learning of large sample hydrology using graph neural networks. Water Resources Research 57, 12 (2021), e2021WR030394.","journal-title":"Water Resources Research"},{"key":"e_1_3_2_255_2","doi-asserted-by":"crossref","unstructured":"Siqi Sun Yu Cheng Zhe Gan and Jingjing Liu. 2019. Patient knowledge distillation for bert model compression. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) . Association for Computational Linguistics Hong Kong 4323\u20134332.","DOI":"10.18653\/v1\/D19-1441"},{"issue":"3","key":"e_1_3_2_256_2","first-page":"818","article-title":"Disease prediction via graph neural networks","volume":"25","author":"Sun Zhenchao","year":"2020","unstructured":"Zhenchao Sun, Hongzhi Yin, Hongxu Chen, Tong Chen, Lizhen Cui, and Fan Yang. 2020. Disease prediction via graph neural networks. IEEE Journal of Biomedical and Health Informatics 25, 3 (2020), 818\u2013826.","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"e_1_3_2_257_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jii.2019.100106"},{"key":"e_1_3_2_258_2","first-page":"1","volume-title":"Proceedings of the 2015 IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)","author":"Tang Leroy Zi Wei","year":"2015","unstructured":"Leroy Zi Wei Tang, Kian Sin Ang, Mohamad Amirul, Maricar Bin Mohamed Yusoff, Chee Keong Tng, Muhammad Danial Bin Mohamed Alyas, Joo Ghee Lim, Phyoe Kyaw Kyaw, and Fachmin Folianto. 2015. Augmented reality control home (ARCH) for disabled and elderlies. In Proceedings of the 2015 IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP). IEEE, 1\u20132."},{"key":"e_1_3_2_259_2","volume-title":"Proceedings of the 21th International Conference on Information Processing in Sensor Networks","author":"Tang Mingyue","year":"2022","unstructured":"Mingyue Tang, Guimin Dong, Jamie Zoellner, Brendan Bowman, Emaad Abel-Rahman, and Mehdi Boukhechba. 2022. Using ubiquitous mobile sensing and temporal sensor-relation graph neural network to predict fluid intake of end stage kidney patients. In Proceedings of the 21th International Conference on Information Processing in Sensor Networks."},{"key":"e_1_3_2_260_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Tang Mingyue","year":"2022","unstructured":"Mingyue Tang, Carl Yang, and Pan Li. 2022. Graph auto-encoder via neighborhood wasserstein reconstruction. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_2_261_2","unstructured":"K\u00fcr\u015fat Tekb\u0131y\u0131k G\u00fcne\u015f Karabulut Kurt Ali R\u0131za Ekti and Halim Yanikomeroglu. 2021. Graph attention networks for channel estimation in RIS-assisted satellite IoT communications. arXiv:2104.00735. Retrieved from https:\/\/arxiv.org\/abs\/2104.00735."},{"key":"e_1_3_2_262_2","first-page":"671","volume-title":"Proceedings of the Conference on Robot Learning","author":"Tolstaya Ekaterina","year":"2020","unstructured":"Ekaterina Tolstaya, Fernando Gama, James Paulos, George Pappas, Vijay Kumar, and Alejandro Ribeiro. 2020. Learning decentralized controllers for robot swarms with graph neural networks. In Proceedings of the Conference on Robot Learning. PMLR, 671\u2013682."},{"key":"e_1_3_2_263_2","doi-asserted-by":"publisher","DOI":"10.1109\/IROS51168.2021.9636675"},{"key":"e_1_3_2_264_2","doi-asserted-by":"crossref","first-page":"1624","DOI":"10.1109\/ICSENS.2011.6127374","volume-title":"Proceedings of the SENSORS, 2011 IEEE","author":"Trinchero D.","year":"2011","unstructured":"D. Trinchero, R. Stefanelli, D. Brunazzi, A. Casalegno, M. Durando, and A. Galardini. 2011. Integration of smart house sensors into a fully networked (web) environment. In Proceedings of the SENSORS, 2011 IEEE. IEEE, 1624\u20131627."},{"key":"e_1_3_2_265_2","doi-asserted-by":"crossref","unstructured":"Md Zia Uddin and Ahmet Soylu. 2021. Human activity recognition using wearable sensors discriminant analysis and long short-term memory-based neural structured learning. Scientific Reports 11 1 (2021) 16455.","DOI":"10.1038\/s41598-021-95947-y"},{"key":"e_1_3_2_266_2","first-page":"143","volume-title":"Proceedings of the International Conference on Information and Communication Technologies for Ageing Well and e-Health","volume":"2","author":"Vavoulas George","year":"2016","unstructured":"George Vavoulas, Charikleia Chatzaki, Thodoris Malliotakis, Matthew Pediaditis, and Manolis Tsiknakis. 2016. The mobiact dataset: Recognition of activities of daily living using smartphones. In Proceedings of the International Conference on Information and Communication Technologies for Ageing Well and e-Health, Vol. 2. SciTePress, 143\u2013151."},{"key":"e_1_3_2_267_2","unstructured":"Petar Veli\u010dkovi\u0107 Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Lio and Yoshua Bengio. 2018. Graph attention networks. International Conference on Learning Representations ."},{"key":"e_1_3_2_268_2","first-page":"20","article-title":"Graph attention networks","volume":"1050","author":"Velickovic Petar","year":"2017","unstructured":"Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. Stat 1050 (2017), 20.","journal-title":"Stat"},{"issue":"8","key":"e_1_3_2_269_2","doi-asserted-by":"crossref","first-page":"3152","DOI":"10.1109\/TITS.2019.2929020","article-title":"Deep learning for intelligent transportation systems: A survey of emerging trends","volume":"21","author":"Veres Matthew","year":"2019","unstructured":"Matthew Veres and Medhat Moussa. 2019. Deep learning for intelligent transportation systems: A survey of emerging trends. IEEE Transactions on Intelligent transportation systems 21, 8 (2019), 3152\u20133168.","journal-title":"IEEE Transactions on Intelligent transportation systems"},{"issue":"1","key":"e_1_3_2_270_2","first-page":"173","article-title":"IoT based smart health monitoring with CNN using edge computing","volume":"22","author":"Vimal S.","year":"2021","unstructured":"S. Vimal, Y. Harold Robinson, Seifedine Kadry, Hoang Viet Long, and Yunyoung Nam. 2021. IoT based smart health monitoring with CNN using edge computing. Journal of Internet Technology 22, 1 (2021), 173\u2013185.","journal-title":"Journal of Internet Technology"},{"key":"e_1_3_2_271_2","unstructured":"Anoushka Vyas and Sambaran Bandyopadhyay. 2020. Semi-supervised soil moisture prediction through graph neural networks. arXiv:2012.03506. Retrieved from https:\/\/arxiv.org\/abs\/2012.03506."},{"key":"e_1_3_2_272_2","first-page":"367","volume-title":"Proceedings of the Machine Learning for Health","author":"Wagh Neeraj","year":"2020","unstructured":"Neeraj Wagh and Yogatheesan Varatharajah. 2020. Eeg-gcnn: Augmenting electroencephalogram-based neurological disease diagnosis using a domain-guided graph convolutional neural network. In Proceedings of the Machine Learning for Health. PMLR, 367\u2013378."},{"key":"e_1_3_2_273_2","unstructured":"Lilapati Waikhom and Ripon Patgiri. 2021. Graph neural networks: Methods applications and opportunities. arXiv:2108.10733. Retrieved from https:\/\/arxiv.org\/abs\/2108.10733."},{"key":"e_1_3_2_274_2","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441731"},{"key":"e_1_3_2_275_2","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1145\/3397536.3422208","volume-title":"Proceedings of the 28th International Conference on Advances in Geographic Information Systems","author":"Wang Shuo","year":"2020","unstructured":"Shuo Wang, Yanran Li, Jiang Zhang, Qingye Meng, Lingwei Meng, and Fei Gao. 2020. PM2. 5-GNN: A domain knowledge enhanced graph neural network for PM2. 5 forecasting. In Proceedings of the 28th International Conference on Advances in Geographic Information Systems. 163\u2013166."},{"key":"e_1_3_2_276_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Wang Tingwu","year":"2018","unstructured":"Tingwu Wang, Renjie Liao, Jimmy Ba, and Sanja Fidler. 2018. Nervenet: Learning structured policy with graph neural networks. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_2_277_2","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2020.2970550"},{"key":"e_1_3_2_278_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-64243-3_18"},{"key":"e_1_3_2_279_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10514-021-09997-2"},{"key":"e_1_3_2_280_2","doi-asserted-by":"crossref","first-page":"538","DOI":"10.1109\/ICUS50048.2020.9274913","volume-title":"Proceedings of the 2020 3rd International Conference on Unmanned Systems (ICUS)","author":"Wang Zhichao","year":"2020","unstructured":"Zhichao Wang, Chang Wang, and Yifeng Niu. 2020. Mixed-initiative manned-unmanned teamwork using coactive design and graph neural network. In Proceedings of the 2020 3rd International Conference on Unmanned Systems (ICUS). IEEE, 538\u2013543."},{"key":"e_1_3_2_281_2","article-title":"Mobile sensing in the COVID-19 era: A review","author":"Wang Zhiyuan","year":"2022","unstructured":"Zhiyuan Wang, Haoyi Xiong, Mingyue Tang, Mehdi Boukhechba, Tabor E. Flickinger, and Laura E. Barnes. 2022. Mobile sensing in the COVID-19 era: A review. Health Data Science (2022).","journal-title":"Health Data Science"},{"key":"e_1_3_2_282_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3161046"},{"issue":"11","key":"e_1_3_2_283_2","doi-asserted-by":"crossref","first-page":"9045","DOI":"10.1109\/JIOT.2021.3055977","article-title":"Remaining useful life prediction of iiot-enabled complex industrial systems with hybrid fusion of multiple information sources","volume":"8","author":"Wen Pengfei","year":"2021","unstructured":"Pengfei Wen, Yong Li, Shaowei Chen, and Shuai Zhao. 2021. Remaining useful life prediction of iiot-enabled complex industrial systems with hybrid fusion of multiple information sources. IEEE Internet of Things Journal 8, 11 (2021), 9045\u20139058.","journal-title":"IEEE Internet of Things Journal"},{"issue":"3","key":"e_1_3_2_284_2","doi-asserted-by":"crossref","first-page":"4640","DOI":"10.1109\/LRA.2021.3068925","article-title":"PTP: Parallelized tracking and prediction with graph neural networks and diversity sampling","volume":"6","author":"Weng Xinshuo","year":"2021","unstructured":"Xinshuo Weng, Ye Yuan, and Kris Kitani. 2021. PTP: Parallelized tracking and prediction with graph neural networks and diversity sampling. IEEE Robotics and Automation Letters 6, 3 (2021), 4640\u20134647.","journal-title":"IEEE Robotics and Automation Letters"},{"key":"e_1_3_2_285_2","doi-asserted-by":"publisher","DOI":"10.1561\/2200000096"},{"key":"e_1_3_2_286_2","first-page":"27","volume-title":"Proceedings of the Graph Neural Networks: Foundations, Frontiers, and Applications","author":"Wu Lingfei","year":"2022","unstructured":"Lingfei Wu, Peng Cui, Jian Pei, Liang Zhao, and Le Song. 2022. Graph neural networks. In Proceedings of the Graph Neural Networks: Foundations, Frontiers, and Applications. Springer, 27\u201337."},{"key":"e_1_3_2_287_2","doi-asserted-by":"crossref","unstructured":"Shiwen Wu Fei Sun Wentao Zhang and Bin Cui. 2020. Graph neural networks in recommender systems: A survey. ACM Computing Surveys 55 5 (2022) 1\u201337.","DOI":"10.1145\/3535101"},{"key":"e_1_3_2_288_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3094295"},{"key":"e_1_3_2_289_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"e_1_3_2_290_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403118"},{"key":"e_1_3_2_291_2","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2021.3076021"},{"key":"e_1_3_2_292_2","doi-asserted-by":"publisher","DOI":"10.1145\/3451394"},{"key":"e_1_3_2_293_2","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/8738613"},{"key":"e_1_3_2_294_2","first-page":"1","volume-title":"Proceedings of the 2021 7th IEEE Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS)","author":"Xu Aidong","year":"2021","unstructured":"Aidong Xu, Tao Wu, Yunan Zhang, Zhiwei Hu, and Yixin Jiang. 2021. Graph-based time series edge anomaly detection in smart grid. In Proceedings of the 2021 7th IEEE Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS). IEEE, 1\u20136."},{"key":"e_1_3_2_295_2","unstructured":"Jiahui Xu Ling Chen Mingqi Lv Chaoqun Zhan Sanjian Chen and Jian Chang. 2021. HighAir: A hierarchical graph neural network-based air quality forecasting method. arXiv:2101.04264. Retrieved from https:\/\/arxiv.org\/abs\/2101.04264."},{"issue":"4","key":"e_1_3_2_296_2","first-page":"601","article-title":"Surface water quality prediction model based on graph neural network","volume":"55","author":"Xu Jia-hui","year":"2021","unstructured":"Jia-hui Xu, Jing-chang Wang, Ling Chen, and Yong Wu. 2021. Surface water quality prediction model based on graph neural network. Journal of ZheJiang University (Engineering Science) 55, 4 (2021), 601\u2013607.","journal-title":"Journal of ZheJiang University (Engineering Science)"},{"key":"e_1_3_2_297_2","unstructured":"Keyulu Xu Weihua Hu Jure Leskovec and Stefanie Jegelka. 2018. How powerful are graph neural networks? International Conference on Learning Representations ."},{"key":"e_1_3_2_298_2","article-title":"Conditional structure generation through graph variational generative adversarial nets","volume":"32","author":"Yang Carl","year":"2019","unstructured":"Carl Yang, Peiye Zhuang, Wenhan Shi, Alan Luu, and Pan Li. 2019. Conditional structure generation through graph variational generative adversarial nets. Advances in Neural Information Processing Systems 32 (2019).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_299_2","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313635"},{"key":"e_1_3_2_300_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2014.2327053"},{"key":"e_1_3_2_301_2","doi-asserted-by":"publisher","DOI":"10.3390\/s21020452"},{"key":"e_1_3_2_302_2","first-page":"1","volume-title":"Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN)","author":"Yang Yilong","year":"2018","unstructured":"Yilong Yang, Qingfeng Wu, Ming Qiu, Yingdong Wang, and Xiaowei Chen. 2018. Emotion recognition from multi-channel EEG through parallel convolutional recurrent neural network. In Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, 1\u20137."},{"key":"e_1_3_2_303_2","doi-asserted-by":"publisher","DOI":"10.1145\/1869790.1869861"},{"issue":"6","key":"e_1_3_2_304_2","doi-asserted-by":"crossref","first-page":"2140","DOI":"10.3390\/s21062140","article-title":"Sensor and sensor fusion technology in autonomous vehicles: A review","volume":"21","year":"2021","unstructured":"De Jong Yeong, Gustavo Velasco-Hernandez, John Barry, and Joseph Walsh. 2021. Sensor and sensor fusion technology in autonomous vehicles: A review. Sensors 21, 6 (2021), 2140.","journal-title":"Sensors"},{"key":"e_1_3_2_305_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2021.3054840"},{"key":"e_1_3_2_306_2","doi-asserted-by":"publisher","DOI":"10.1145\/3460427"},{"key":"e_1_3_2_307_2","first-page":"703","volume-title":"Proceedings of the 2013 3rd International Conference on Consumer Electronics, Communications and Networks","author":"Yongqing Gao","year":"2013","unstructured":"Gao Yongqing and Shang Dan. 2013. The research of home intelligent power system based on ZigBee. In Proceedings of the 2013 3rd International Conference on Consumer Electronics, Communications and Networks. IEEE, 703\u2013706."},{"key":"e_1_3_2_308_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3068798"},{"key":"e_1_3_2_309_2","doi-asserted-by":"crossref","unstructured":"Bing Yu Haoteng Yin and Zhanxing Zhu. 2018. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI\u201918) . AAAI Press 3634\u20133640.","DOI":"10.24963\/ijcai.2018\/505"},{"key":"e_1_3_2_310_2","first-page":"846","volume-title":"Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)","author":"Yu Xinchi","year":"2018","unstructured":"Xinchi Yu, Michael Paul, Christoph Hoog Antink, Boudewijn Venema, Vladimir Blazek, Cornelius Bollheimer, Steffen Leonhardt, and Daniel Teichmann. 2018. Non-contact remote measurement of heart rate variability using near-infrared photoplethysmography imaging. In Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 846\u2013849."},{"key":"e_1_3_2_311_2","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/1684726"},{"key":"e_1_3_2_312_2","doi-asserted-by":"publisher","DOI":"10.1007\/s41019-020-00151-z"},{"key":"e_1_3_2_313_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3204236"},{"key":"e_1_3_2_314_2","doi-asserted-by":"publisher","DOI":"10.1002\/ett.4427"},{"key":"e_1_3_2_315_2","doi-asserted-by":"publisher","DOI":"10.1145\/3411816"},{"key":"e_1_3_2_316_2","unstructured":"Bin Zhang Yunpeng Bai Zhiwei Xu Dapeng Li and Guoliang Fan. 2022. Efficient cooperation strategy generation in multi-agent video games via hypergraph neural network. arXiv:2203.03265. Retrieved from https:\/\/arxiv.org\/abs\/2203.03265."},{"key":"e_1_3_2_317_2","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2014.6871668"},{"key":"e_1_3_2_318_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2020.102851"},{"key":"e_1_3_2_319_2","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1109\/ICME.2019.00078","volume-title":"Proceedings of the 2019 IEEE International Conference on Multimedia and Expo (ICME)","author":"Zhang Han","year":"2019","unstructured":"Han Zhang, Yonghong Song, and Yuanlin Zhang. 2019. Graph convolutional LSTM model for skeleton-based action recognition. In Proceedings of the 2019 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 412\u2013417."},{"key":"e_1_3_2_320_2","doi-asserted-by":"crossref","first-page":"1536","DOI":"10.1109\/ICDE51399.2021.00136","volume-title":"Proceedings of the 2021 IEEE 37th International Conference on Data Engineering (ICDE)","author":"Zhang Jun","year":"2021","unstructured":"Jun Zhang, Chen Gao, Depeng Jin, and Yong Li. 2021. Group-buying recommendation for social e-commerce. In Proceedings of the 2021 IEEE 37th International Conference on Data Engineering (ICDE). IEEE, 1536\u20131547."},{"key":"e_1_3_2_321_2","doi-asserted-by":"publisher","DOI":"10.1145\/2370216.2370438"},{"issue":"2205","key":"e_1_3_2_322_2","doi-asserted-by":"crossref","first-page":"20170457","DOI":"10.1098\/rspa.2017.0457","article-title":"Cautionary tales on air-quality improvement in Beijing","volume":"473","author":"Zhang Shuyi","year":"2017","unstructured":"Shuyi Zhang, Bin Guo, Anlan Dong, Jing He, Ziping Xu, and Song Xi Chen. 2017. Cautionary tales on air-quality improvement in Beijing. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 473, 2205 (2017), 20170457.","journal-title":"Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences"},{"key":"e_1_3_2_323_2","first-page":"1186","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"34","author":"Zhang Weijia","year":"2020","unstructured":"Weijia Zhang, Hao Liu, Yanchi Liu, Jingbo Zhou, and Hui Xiong. 2020. Semi-supervised hierarchical recurrent graph neural network for city-wide parking availability prediction. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 1186\u20131193."},{"key":"e_1_3_2_324_2","unstructured":"Wentao Zhang Zeang Sheng Yuezihan Jiang Yikuan Xia Jun Gao Zhi Yang and Bin Cui. 2021. Evaluating deep graph neural networks. arXiv:2108.00955. Retrieved from https:\/\/arxiv.org\/abs\/2108.00955."},{"key":"e_1_3_2_325_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2902865"},{"key":"e_1_3_2_326_2","doi-asserted-by":"publisher","DOI":"10.1109\/SECON48991.2020.9158447"},{"key":"e_1_3_2_327_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.2981333"},{"key":"e_1_3_2_328_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106273"},{"key":"e_1_3_2_329_2","first-page":"6882","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Zhao Rui","year":"2019","unstructured":"Rui Zhao, Kang Wang, Hui Su, and Qiang Ji. 2019. Bayesian graph convolution lstm for skeleton based action recognition. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. 6882\u20136892."},{"issue":"3","key":"e_1_3_2_330_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2629592","article-title":"Urban computing: Concepts, methodologies, and applications","volume":"5","author":"Zheng Yu","year":"2014","unstructured":"Yu Zheng, Licia Capra, Ouri Wolfson, and Hai Yang. 2014. Urban computing: Concepts, methodologies, and applications. ACM Transactions on Intelligent Systems and Technology (TIST) 5, 3 (2014), 1\u201355.","journal-title":"ACM Transactions on Intelligent Systems and Technology (TIST)"},{"key":"e_1_3_2_331_2","first-page":"558","volume-title":"Proceedings of the 2019 6th International Conference on Systems and Informatics (ICSAI)","author":"Zheng Yin","year":"2019","unstructured":"Yin Zheng, Dongping Zhang, Li Yang, and Zhihong Zhou. 2019. Fall detection and recognition based on GCN and 2D Pose. In Proceedings of the 2019 6th International Conference on Systems and Informatics (ICSAI). IEEE, 558\u2013562."},{"key":"e_1_3_2_332_2","doi-asserted-by":"publisher","DOI":"10.1109\/TAFFC.2020.2994159"},{"issue":"19","key":"e_1_3_2_333_2","first-page":"12741","article-title":"Machine learning: New ideas and tools in environmental science and engineering","volume":"55","year":"2021","unstructured":"Shifa Zhong, Kai Zhang, Majid Bagheri, Joel G. Burken, April Gu, Baikun Li, Xingmao Ma, Babetta L. Marrone, Zhiyong Jason Ren, Joshua Schrier, Wei Shi, Haoyue Tan, Tianbao Wang, Xu Wang, Bryan M. Wong, Xusheng Xiao, Xiong Yu, Jun-Jie Zhu, and Huichun Zhang. 2021. Machine learning: New ideas and tools in environmental science and engineering. Environmental Science & Technology 55, 19 (2021), 12741\u201312754.","journal-title":"Environmental Science & Technology"},{"key":"e_1_3_2_334_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiopen.2021.01.001"},{"key":"e_1_3_2_335_2","doi-asserted-by":"crossref","unstructured":"Lifeng Zhou Vishnu D. Sharma Qingbiao Li Amanda Prorok Alejandro Ribeiro and Vijay Kumar. 2021. Graph neural networks for decentralized multi-robot submodular action selection. arXiv:2105.08601. Retrieved from https:\/\/arxiv.org\/abs\/2105.08601.","DOI":"10.1109\/SSRR56537.2022.10018712"},{"key":"e_1_3_2_336_2","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2022.3141661"},{"key":"e_1_3_2_337_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2022.3145979"},{"key":"e_1_3_2_338_2","unstructured":"Weicheng Zhu and Narges Razavian. 2019. Graph neural network on electronic health records for predicting Alzheimer\u2019s disease. ArXiv abs\/1912.03761."},{"key":"e_1_3_2_339_2","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.3046627"}],"container-title":["ACM Transactions on Sensor Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3565973","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3565973","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:08:32Z","timestamp":1750183712000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3565973"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,5]]},"references-count":338,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,5,31]]}},"alternative-id":["10.1145\/3565973"],"URL":"https:\/\/doi.org\/10.1145\/3565973","relation":{},"ISSN":["1550-4859","1550-4867"],"issn-type":[{"value":"1550-4859","type":"print"},{"value":"1550-4867","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,5]]},"assertion":[{"value":"2022-04-19","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-09-26","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-04-05","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}