{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T16:08:10Z","timestamp":1781280490304,"version":"3.54.1"},"reference-count":73,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T00:00:00Z","timestamp":1734652800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>Social media platforms, including X, Facebook, and Instagram, host millions of daily users, giving rise to bots automated programs disseminating misinformation and ideologies with tangible real-world consequences. While bot detection in platform X has been the area of many deep learning models with adequate results, most approaches neglect the graph structure of social media relationships and often rely on hand-engineered architectures. Our work introduces the implementation of a Neural Architecture Search (NAS) technique, namely Deep and Flexible Graph Neural Architecture Search (DFG-NAS), tailored to Relational Graph Convolutional Neural Networks (RGCNs) in the task of bot detection in platform X. Our model constructs a graph that incorporates both the user relationships and their metadata. Then, DFG-NAS is adapted to automatically search for the optimal configuration of Propagation and Transformation functions in the RGCNs. Our experiments are conducted on the TwiBot-20 dataset, constructing a graph with 229,580 nodes and 227,979 edges. We study the five architectures with the highest performance during the search and achieve an accuracy of 85.7%, surpassing state-of-the-art models. Our approach not only addresses the bot detection challenge but also advocates for the broader implementation of NAS models in neural network design automation.<\/jats:p>","DOI":"10.3389\/frai.2024.1509179","type":"journal-article","created":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T06:29:15Z","timestamp":1734676155000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["A graph neural architecture search approach for identifying bots in social media"],"prefix":"10.3389","volume":"7","author":[{"given":"Georgios","family":"Tzoumanekas","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michail","family":"Chatzianastasis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Loukas","family":"Ilias","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"George","family":"Kiokes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John","family":"Psarras","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dimitris","family":"Askounis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,12,20]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"6662","DOI":"10.3390\/su15086662","article-title":"Twitter bot detection using diverse content features and applying machine learning algorithms","volume":"15","author":"Alarfaj","year":"2023","journal-title":"Sustainability"},{"key":"B2","doi-asserted-by":"publisher","first-page":"102479","DOI":"10.1016\/j.jnca.2019.102479","article-title":"An efficient reinforcement learning-based botnet detection approach","volume":"150","author":"Alauthman","year":"2020","journal-title":"J. Netw. Comput. Appl"},{"key":"B3","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1145\/3308560.3316504","article-title":"\u201cDetect me if you can: spam bot detection using inductive representation learning,\u201d","volume-title":"Companion Proceedings of The 2019 World Wide Web Conference, WWW '19","author":"Ali Alhosseini","year":"2019"},{"key":"B4","doi-asserted-by":"publisher","first-page":"8117","DOI":"10.3390\/app12168117","article-title":"Bot-mgat: a transfer learning model based on a multi-view graph attention network to detect social bots","volume":"12","author":"Alothali","year":"2022","journal-title":"Appl. Sci"},{"key":"B5","doi-asserted-by":"publisher","first-page":"102303","DOI":"10.1016\/j.ipm.2020.102303","article-title":"How many bots in Russian troll tweets?","volume":"57","author":"Alsmadi","year":"2020","journal-title":"Inf. Process. Manag"},{"key":"B6","doi-asserted-by":"publisher","first-page":"103146","DOI":"10.1016\/j.ipm.2022.103146","article-title":"Multi-view co-attention network for fake news detection by modeling topic-specific user and news source credibility","volume":"60","author":"Bazmi","year":"2023","journal-title":"Inf. Process. Manag"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.5210\/fm.v21i11.7090","article-title":"Social bots distort the 2016 U.S. presidential election online discussion","author":"Bessi","year":"2016","journal-title":"First Monday"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1109\/TransAI54797.2022.00022","article-title":"\u201cTwitter bot detection using social network analysis,\u201d","author":"Bui","year":"2022","journal-title":"2022 Fourth International Conference on Transdisciplinary AI (TransAI)"},{"key":"B9","doi-asserted-by":"crossref","first-page":"6653","DOI":"10.1109\/CVPR46437.2021.00659","article-title":"\u201cRethinking graph neural architecture search from message-passing,\u201d","volume-title":"2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Cai","year":"2021"},{"key":"B10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/ICASSP49357.2023.10096579","article-title":"\u201cNeural architecture search with multimodal fusion methods for diagnosing dementia,\u201d","volume-title":"ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","author":"Chatzianastasis","year":"2023"},{"key":"B11","doi-asserted-by":"crossref","first-page":"817","DOI":"10.1109\/ICDM.2016.0096","article-title":"\u201cDebot: Twitter bot detection via warped correlation,\u201d","volume-title":"2016 IEEE 16th International Conference on Data Mining (ICDM)","author":"Chavoshi","year":"2016"},{"key":"B12","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1109\/TDSC.2017.2681672","article-title":"Social fingerprinting: detection of spambot groups through dna-inspired behavioral modeling","volume":"15","author":"Cresci","year":"2017","journal-title":"IEEE Trans. Dependable Secure Comput"},{"key":"B13","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1145\/2872518.2889302","article-title":"\u201cBotornot: a system to evaluate social bots,\u201d","volume-title":"Proceedings of the 25th International Conference Companion on World Wide Web, WWW '16 Companion","author":"Davis","year":"2016"},{"key":"B14","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1186\/s40537-023-00796-3","article-title":"Detecting bots in social-networks using node and structural embeddings","volume":"10","author":"Dehghan","year":"2023","journal-title":"J. Big Data"},{"key":"B15","doi-asserted-by":"publisher","first-page":"102245","DOI":"10.1016\/j.datak.2023.102245","article-title":"Caleb: a conditional adversarial learning framework to enhance bot detection","volume":"149","author":"Dimitriadis","year":"2024","journal-title":"Data Knowl. Eng"},{"key":"B16","doi-asserted-by":"publisher","first-page":"102317","DOI":"10.1016\/j.ipm.2020.102317","article-title":"Similcatch: enhanced social spammers detection on Twitter using markov random fields","volume":"57","author":"El-Mawass","year":"2020","journal-title":"Inf. Process. Manag"},{"key":"B17","first-page":"3977","article-title":"\u201cHeterogeneity-aware Twitter bot detection with relational graph transformers,\u201d","volume-title":"AAAI Conference on Artificial Intelligence, Vol. 36","author":"Feng","year":"2022"},{"key":"B18","article-title":"\u201cTwibot-22: towards graph-based Twitter bot detection,\u201d","volume-title":"Proceedings of the 36th International Conference on Neural Information Processing Systems, NIPS '22","author":"Feng","year":"2024"},{"key":"B19","first-page":"3808","article-title":"\u201cSatar: a self-supervised approach to Twitter account representation learning and its application in bot detection,\u201d","volume-title":"Proceedings of the 30th ACM International Conference on Information; Knowledge Management, CIKM '21","author":"Feng","year":""},{"key":"B20","first-page":"4485","article-title":"\u201cTwibot-20: a comprehensive Twitter bot detection benchmark,\u201d","volume-title":"Proceedings of the 30th ACM International Conference on Information &Knowledge Management, CIKM '21","author":"Feng","year":""},{"key":"B21","first-page":"236","article-title":"\u201cBotrgcn: Twitter bot detection with relational graph convolutional networks,\u201d","volume-title":"Proceedings of the 2021 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM '21","author":"Feng","year":""},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.5210\/fm.v25i6.10633","author":"Ferrara","year":"2020","journal-title":"What types of COVID-19 conspiracies are populated by Twitter bots"},{"key":"B23","doi-asserted-by":"publisher","first-page":"1403","DOI":"10.24963\/ijcai.2020\/195","article-title":"\u201cGraph neural architecture search,\u201d","author":"Gao","year":"2021","journal-title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI'20"},{"key":"B24","doi-asserted-by":"publisher","first-page":"110834","DOI":"10.1016\/j.knosys.2023.110834","article-title":"Context-aware attention layers coupled with optimal transport domain adaptation and multimodal fusion methods for recognizing dementia from spontaneous speech","volume":"277","author":"Ilias","year":"","journal-title":"Knowl.-Based Syst"},{"key":"B25","doi-asserted-by":"publisher","DOI":"10.1016\/j.osnem.2023.100270","article-title":"Multitask learning for recognizing stress and depression in social media","author":"Ilias","year":"","journal-title":"Online Soci. Netw. Media"},{"key":"B26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/BHI56158.2022.9926818","article-title":"\u201cA multimodal approach for dementia detection from spontaneous speech with tensor fusion layer,\u201d","volume-title":"2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)","author":"Ilias","year":"2022"},{"key":"B27","doi-asserted-by":"publisher","first-page":"7320","DOI":"10.1109\/TIFS.2024.3435138","article-title":"Multimodal detection of bots on x (Twitter) using transformers","volume":"19","author":"Ilias","year":"","journal-title":"IEEE Trans. Inf. Forensics Secur"},{"key":"B28","doi-asserted-by":"publisher","first-page":"1979","DOI":"10.1109\/TCSS.2023.3283009","article-title":"Calibration of transformer-based models for identifying stress and depression in social media","volume":"11","author":"Ilias","year":"","journal-title":"IEEE Trans. Comput. Soc. Syst"},{"key":"B29","doi-asserted-by":"publisher","first-page":"107360","DOI":"10.1016\/j.asoc.2021.107360","article-title":"Detecting malicious activity in Twitter using deep learning techniques","volume":"107","author":"Ilias","year":"2021","journal-title":"Appl. Soft Comput"},{"key":"B30","doi-asserted-by":"crossref","first-page":"1346","DOI":"10.1109\/BigData50022.2020.9378060","article-title":"\u201cGraph neural network architecture search for molecular property prediction,\u201d","volume-title":"2020 IEEE International Conference on Big Data (Big Data)","author":"Jiang","year":"2020"},{"key":"B31","doi-asserted-by":"publisher","first-page":"1384","DOI":"10.1016\/j.camwa.2012.01.034","article-title":"Online game bot detection based on party-play log analysis","volume":"65","author":"Kang","year":"2013","journal-title":"Comp. Math. Appl"},{"key":"B32","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1007\/s13278-024-01360-4","article-title":"Depression detection in social media posts using transformer-based models and auxiliary features","volume":"14","author":"Kerasiotis","year":"2024","journal-title":"Soc. Netw. Anal. Mining"},{"key":"B33","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1186\/s40537-021-00552-5","article-title":"An unsupervised method for social network spammer detection based on user information interests","volume":"9","author":"Koggalahewa","year":"2022","journal-title":"J. Big Data"},{"key":"B34","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1016\/j.ins.2018.08.019","article-title":"Deep neural networks for bot detection","volume":"467","author":"Kudugunta","year":"2018","journal-title":"Inf. Sci"},{"key":"B35","doi-asserted-by":"publisher","first-page":"102126","DOI":"10.1016\/j.ipm.2019.102126","article-title":"You talkin' to me? Exploring human\/bot communication patterns during riot events","volume":"57","author":"Ku\u0161en","year":"2020","journal-title":"Inf. Process. Manag"},{"key":"B36","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1609\/icwsm.v5i1.14106","article-title":"Seven months with the devils: a long-term study of content polluters on Twitter","volume":"5","author":"Lee","year":"2021","journal-title":"Proc. Int. AAAI Conf. Web Soc. Media"},{"key":"B37","doi-asserted-by":"publisher","first-page":"8510","DOI":"10.1609\/aaai.v35i10.17033","article-title":"One-shot graph neural architecture search with dynamic search space","volume":"35","author":"Li","year":"2021","journal-title":"Proc. AAAI Conf. Artif. Intell"},{"key":"B38","doi-asserted-by":"publisher","first-page":"106300","DOI":"10.1016\/j.engappai.2023.106300","article-title":"Meta-gnas: meta-reinforcement learning for graph neural architecture search","volume":"123","author":"Li","year":"2023","journal-title":"Eng. Appl. Artif. Intell"},{"key":"B39","first-page":"485","article-title":"\u201cBotmoe: Twitter bot detection with community-aware mixtures of modal-specific experts,\u201d","volume-title":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '23","author":"Liu","year":"2023"},{"key":"B40","first-page":"1","article-title":"\u201cBotnet detection based on network flow analysis using inverse statistics,\u201d","volume-title":"2022 17th Iberian Conference on Information Systems and Technologies (CISTI)","author":"Lopes","year":"2022"},{"key":"B41","doi-asserted-by":"publisher","first-page":"127509","DOI":"10.1016\/j.neucom.2024.127509","article-title":"Guided evolutionary neural architecture search with efficient performance estimation","volume":"584","author":"Lopes","year":"2024","journal-title":"Neurocomputing"},{"key":"B42","doi-asserted-by":"publisher","first-page":"103454","DOI":"10.1016\/j.ipm.2023.103454","article-title":"Cyberbullying detection for low-resource languages and dialects: review of the state of the art","volume":"60","author":"Mahmud","year":"2023","journal-title":"Inf. Process. Manag"},{"key":"B43","doi-asserted-by":"crossref","first-page":"1485","DOI":"10.1109\/BigData55660.2022.10020363","article-title":"\u201cMulbot: unsupervised bot detection based on multivariate time series,\u201d","volume-title":"2022 IEEE International Conference on Big Data (Big Data)","author":"Mannocci","year":"2022"},{"key":"B44","doi-asserted-by":"publisher","first-page":"101492","DOI":"10.1016\/j.tsc.2024.101492","article-title":"How does social media knowledge help in combating fake news? Testing a structural equation model","volume":"52","author":"Mi","year":"2024","journal-title":"Think. Skills Creat"},{"key":"B45","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.ins.2013.11.016","article-title":"Twitter spammer detection using data stream clustering","volume":"260","author":"Miller","year":"2014","journal-title":"Inf. Sci"},{"key":"B46","first-page":"467","article-title":"\u201cBotwalk: efficient adaptive exploration of Twitter bot networks,\u201d","volume-title":"2017 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","author":"Minnich","year":"2017"},{"key":"B47","doi-asserted-by":"crossref","DOI":"10.1109\/ICSPS.2010.5555837","article-title":"\u201cA high level security mechanism for internet polls,\u201d","volume-title":"2010 2nd International Conference on Signal Processing Systems, Volume 3","author":"Mohammadi","year":"2010"},{"key":"B48","doi-asserted-by":"publisher","first-page":"102140","DOI":"10.1016\/j.ipm.2019.102140","article-title":"Opinion spam detection: using multi-iterative graph-based model","volume":"57","author":"Noekhah","year":"2020","journal-title":"Inf. Process. Manag"},{"key":"B49","volume-title":"Intelligent Systems: 9th Brazilian Conference, BRACIS 2020, Rio Grande, Brazil, October 20\u201323, 2020, Proceedings, Part I","author":"Nunes","year":"2020"},{"key":"B50","doi-asserted-by":"publisher","first-page":"2669","DOI":"10.1609\/aaai.v34i03.5652","article-title":"Learning graph convolutional network for skeleton-based human action recognition by neural searching","volume":"34","author":"Peng","year":"2020","journal-title":"Proc. AAAI Conf. Artif. Intell"},{"key":"B51","doi-asserted-by":"crossref","first-page":"1257","DOI":"10.1145\/3583131.3590452","article-title":"\u201cFast evolutionary neural architecture search by contrastive predictor with linear regions,\u201d","volume-title":"Proceedings of the Genetic and Evolutionary Computation Conference, GECCO '23","author":"Peng","year":"2023"},{"key":"B52","doi-asserted-by":"publisher","first-page":"101771","DOI":"10.1016\/j.is.2021.101771","article-title":"Bot2vec: a general approach of intra-community oriented representation learning for bot detection in different types of social networks","volume":"103","author":"Pham","year":"2022","journal-title":"Inf. Syst"},{"key":"B53","doi-asserted-by":"publisher","first-page":"109725","DOI":"10.1016\/j.comnet.2023.109725","article-title":"Real-time bot infection detection system using dns fingerprinting and machine-learning","volume":"228","author":"Quezada","year":"2023","journal-title":"Comput. Netw"},{"key":"B54","doi-asserted-by":"publisher","first-page":"103360","DOI":"10.1016\/j.ipm.2023.103360","article-title":"A statistical approach for reducing misinformation propagation on Twitter social media","volume":"60","author":"Saxena","year":"2023","journal-title":"Inf. Process. Manag"},{"key":"B55","doi-asserted-by":"publisher","first-page":"107587","DOI":"10.1016\/j.chb.2022.107587","article-title":"Dialog in the echo chamber: fake news framing predicts emotion, argumentation and dialogic social knowledge building in subsequent online discussions","volume":"140","author":"Scheibenzuber","year":"2023","journal-title":"Comput. Human Behav"},{"key":"B56","doi-asserted-by":"publisher","first-page":"101354","DOI":"10.1016\/j.swevo.2023.101354","article-title":"Evolutionary architecture search via adaptive parameter control and gene potential contribution","volume":"82","author":"Shang","year":"2023","journal-title":"Swarm Evol. Comput"},{"key":"B57","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2306.00037","article-title":"Botartist: Twitter bot detection machine learning model based on Twitter suspension","author":"Shevtsov","year":"2023","journal-title":"arXiv"},{"key":"B58","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/ICDSAAI55433.2022.10028860","article-title":"\u201cTwitter bot detection and ranking using supervised machine learning models,\u201d","author":"Sujith","year":"2022","journal-title":"2022 International Conference on Data Science, Agents"},{"key":"B59","doi-asserted-by":"publisher","first-page":"103012","DOI":"10.1016\/j.ipm.2022.103012","article-title":"The language and targets of online trolling: a psycholinguistic approach for social cybersecurity","volume":"59","author":"Uyheng","year":"2022","journal-title":"Inf. Proc. Manag"},{"key":"B60","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1109\/TPS-ISA48467.2019.00021","article-title":"\u201cTwitter bot detection using bidirectional long short-term memory neural networks and word embeddings,\u201d","volume-title":"2019 First IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)","author":"Wei","year":"2019"},{"key":"B61","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1109\/OJCS.2023.3302286","article-title":"Twitter bot detection using neural networks and linguistic embeddings","volume":"4","author":"Wei","year":"2023","journal-title":"IEEE Open J. Comput. Soc"},{"key":"B62","doi-asserted-by":"crossref","first-page":"5833","DOI":"10.1109\/BigData55660.2022.10020983","article-title":"\u201cTwitter bot detection through unsupervised machine learning,\u201d","volume-title":"2022 IEEE International Conference on Big Data (Big Data)","author":"Wu","year":"2022"},{"key":"B63","doi-asserted-by":"publisher","first-page":"103228","DOI":"10.1016\/j.ipm.2022.103228","article-title":"Being my own gatekeeper, how i tell the fake and the real \u2013 fake news perception between typologies and sources","volume":"60","author":"Xu","year":"2023","journal-title":"Inf. Process. Manag"},{"key":"B64","doi-asserted-by":"publisher","first-page":"1511","DOI":"10.1007\/s42001-022-00177-5","article-title":"Botometer 101: social bot practicum for computational social scientists","volume":"5","author":"Yang","year":"2022","journal-title":"J. Comput. Soc. Sci"},{"key":"B65","doi-asserted-by":"publisher","first-page":"1096","DOI":"10.1609\/aaai.v34i01.5460","article-title":"Scalable and generalizable social bot detection through data selection","volume":"34","author":"Yang","year":"2020","journal-title":"Proc. AAAI Conf. Artif. Intell"},{"key":"B66","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3572403","article-title":"Rosgas: adaptive social bot detection with reinforced self-supervised gnn architecture search","volume":"17","author":"Yang","year":"2023","journal-title":"ACM Trans. Web"},{"key":"B67","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2306.12870","article-title":"Hofa: Twitter bot detection with homophily-oriented augmentation and frequency adaptive attention","author":"Ye","year":"2023","journal-title":"arXiv"},{"key":"B68","unstructured":"\u201cDeep and flexible graph neural architecture search,\u201d\n          \n          26362\n          26374\n          \n            \n              Zhang\n              W.\n            \n            \n              Lin\n              Z.\n            \n            \n              Shen\n              Y.\n            \n            \n              Li\n              Y.\n            \n            \n              Yang\n              Z.\n            \n            \n              Cui\n              B.\n            \n          \n          Proceedings of the 39th International Conference on Machine Learning, Volume 162 of Proceedings of Machine Learning Research\n          \n          2022"},{"key":"B69","doi-asserted-by":"publisher","first-page":"129386","DOI":"10.1016\/j.physa.2023.129386","article-title":"How social bots can influence public opinion more effectively: Right connection strategy","volume":"633","author":"Zhang","year":"2024","journal-title":"Phys. A Stat. Mech. Appl"},{"key":"B70","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2008.11652","article-title":"Simplifying architecture search for graph neural network","author":"Zhao","year":"2020","journal-title":"arXiv"},{"key":"B71","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1109\/ICDE51399.2021.00054","article-title":"\u201cSearch to aggregate neighborhood for graph neural network\u201d","volume-title":"2021 IEEE 37th International Conference on Data Engineering (ICDE)","author":"Zhao","year":"2021"},{"key":"B72","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1016\/j.neucom.2021.01.072","article-title":"A neural architecture search method based on gradient descent for remaining useful life estimation","volume":"438","author":"Zhao","year":"2021","journal-title":"Neurocomputing"},{"key":"B73","doi-asserted-by":"publisher","first-page":"1029307","DOI":"10.3389\/fdata.2022.1029307","article-title":"Auto-gnn: neural architecture search of graph neural networks","volume":"5","author":"Zhou","year":"2022","journal-title":"Front. Big Data"}],"container-title":["Frontiers in Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2024.1509179\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T06:29:31Z","timestamp":1734676171000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2024.1509179\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,20]]},"references-count":73,"alternative-id":["10.3389\/frai.2024.1509179"],"URL":"https:\/\/doi.org\/10.3389\/frai.2024.1509179","relation":{},"ISSN":["2624-8212"],"issn-type":[{"value":"2624-8212","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,20]]},"article-number":"1509179"}}