{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T04:10:29Z","timestamp":1781496629711,"version":"3.54.1"},"reference-count":123,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/access.2025.3558752","type":"journal-article","created":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T17:55:59Z","timestamp":1744221359000},"page":"62870-62891","source":"Crossref","is-referenced-by-count":33,"title":["Graph Neural Networks: Architectures, Applications, and Future Directions"],"prefix":"10.1109","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-2910-0273","authenticated-orcid":false,"given":"Valerio","family":"Ponzi","sequence":"first","affiliation":[{"name":"Department of Computer, Control, and Management Engineering, Sapienza University of Rome, Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3336-5853","authenticated-orcid":false,"given":"Christian","family":"Napoli","sequence":"additional","affiliation":[{"name":"Department of Computer, Control, and Management Engineering, Sapienza University of Rome, Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1993","article-title":"Diffusion-convolutional neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"29","author":"Atwood"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-021-00418-8"},{"key":"ref3","article-title":"Neural machine translation by jointly learning to align and translate","author":"Bahdanau","year":"2014","journal-title":"arXiv:1409.0473"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-24628-9_16"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2019.00191"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1209"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/EAIT.2014.11"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.12791"},{"key":"ref9","article-title":"Neural combinatorial optimization with reinforcement learning","author":"Bello","year":"2016","journal-title":"arXiv:1611.09940"},{"key":"ref10","article-title":"Graph convolutional matrix completion","author":"van den Berg","year":"2017","journal-title":"arXiv:1706.02263"},{"key":"ref11","article-title":"How attentive are graph attention networks?","author":"Brody","year":"2021","journal-title":"arXiv:2105.14491"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2017.2693418"},{"key":"ref13","article-title":"Spectral networks and locally connected networks on graphs","author":"Bruna","year":"2013","journal-title":"arXiv:1312.6203"},{"key":"ref14","first-page":"19746","article-title":"Graph neural networks with adaptive readouts","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Buterez"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5747"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.102146"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3155602"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1186\/s13073-021-00845-7"},{"key":"ref19","first-page":"1106","article-title":"Learning steady-states of iterative algorithms over graphs","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Dai"},{"key":"ref20","first-page":"3844","article-title":"Convolutional neural networks on graphs with fast localized spectral filtering","volume-title":"Proc. 30th Int. Conf. Neural Inf. Process. Syst.","author":"Defferrard"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyt.2019.00140"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/MWSCAS.2017.8053243"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TCSII.2019.2891704"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2012.140"},{"key":"ref25","first-page":"1","article-title":"Sheaf hypergraph networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Duta"},{"issue":"43","key":"ref26","first-page":"1","article-title":"Benchmarking graph neural networks","volume":"24","author":"Dwivedi","year":"2023","journal-title":"J. Mach. Learn. Res."},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313488"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/AEMCSE51986.2021.00163"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.5120\/1462-1976"},{"key":"ref30","first-page":"7637","article-title":"Robustness of graph neural networks at scale","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Geisler"},{"key":"ref31","first-page":"1263","article-title":"Neural message passing for quantum chemistry","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Gilmer"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-24797-2_4"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301922"},{"key":"ref35","article-title":"Inductive representation learning on large graphs","author":"Hamilton","year":"2017","journal-title":"arXiv:1706.02216"},{"key":"ref36","article-title":"FedGraphNN: A federated learning system and benchmark for graph neural networks","author":"He","year":"2021","journal-title":"arXiv:2104.07145"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-021-04295-1"},{"key":"ref38","first-page":"22118","article-title":"Open graph benchmark: Datasets for machine learning on graphs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Hu"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16533"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.573"},{"key":"ref41","first-page":"22070","article-title":"NeuroMLR: Robust & reliable route recommendation on road networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Jain"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-020-09825-6"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/ICPHM.2019.8819403"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2020.00630"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1609.02907"},{"key":"ref46","article-title":"Attention, learn to solve routing problems!","author":"Kool","year":"2018","journal-title":"arXiv:1803.08475"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-024-50426-6"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1038\/s41551-022-00942-x"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.acl-long.162"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11691"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.126441"},{"key":"ref52","article-title":"Classifying and understanding financial data using graph neural network","volume-title":"Proc. AAAI Workshop Knowl. Discovery Unstructured Data Financial Services (KDF)","author":"Li"},{"key":"ref53","article-title":"A survey of explainable graph neural networks: Taxonomy and evaluation metrics","author":"Li","year":"2022","journal-title":"arXiv:2207.12599"},{"key":"ref54","article-title":"Gated graph sequence neural networks","author":"Li","year":"2015","journal-title":"arXiv:1511.05493"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3084827"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2020.3025259"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46448-0_8"},{"key":"ref58","article-title":"A critical review of recurrent neural networks for sequence learning","author":"Lipton","year":"2015","journal-title":"arXiv:1506.00019"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583386"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1145\/3616855.3635784"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645685"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W18-6501"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1159"},{"key":"ref64","article-title":"GNN-RAG: Graph neural retrieval for large language model reasoning","author":"Mavromatis","year":"2024","journal-title":"arXiv:2405.20139"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbad431"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2008.2010350"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2004.837783"},{"key":"ref68","article-title":"Efficient estimation of word representations in vector space","author":"Mikolov","year":"2013","journal-title":"arXiv:1301.3781"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1145\/3604915.3608875"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.576"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3615170"},{"key":"ref72","article-title":"TUDataset: A collection of benchmark datasets for learning with graphs","author":"Morris","year":"2020","journal-title":"arXiv:2007.08663"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2896880"},{"key":"ref74","first-page":"2014","article-title":"Learning convolutional neural networks for graphs","volume-title":"Proc. 33rd Int. Conf. Mach. Learn.","author":"Niepert"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2018.2820126"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1109\/GlobalSIP.2018.8646486"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP48485.2024.10446827"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00662"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"ref80","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/B978-0-12-815739-8.00011-0","article-title":"Autoencoders","volume-title":"Machine Learning","author":"Pinaya","year":"2020"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1145\/3234150"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01240-3_25"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220077"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.14778\/3494124.3494128"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1801.01078"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2008.2005605"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.110974"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1109\/72.572108"},{"key":"ref89","doi-asserted-by":"publisher","DOI":"10.1561\/2200000078-3"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/p15-1150"},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741093"},{"issue":"127","key":"ref92","first-page":"1","article-title":"Graph clustering with graph neural networks","volume":"24","author":"Tsitsulin","year":"2023","journal-title":"J. Mach. Learn. Res."},{"key":"ref93","article-title":"Graph attention networks","author":"Veli\u010d kovi\u0107","year":"2017","journal-title":"arXiv:1710.10903"},{"key":"ref94","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN52387.2021.9533711"},{"key":"ref95","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"ref96","doi-asserted-by":"publisher","DOI":"10.1007\/s00138-021-01251-0"},{"key":"ref97","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.330"},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiopen.2021.05.002"},{"key":"ref99","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2024.1388015"},{"key":"ref100","first-page":"1509","article-title":"HyperGCN: A new method for training graph convolutional networks on hypergraphs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Yadati"},{"key":"ref101","first-page":"2111","article-title":"Network representation learning with rich text information","volume-title":"Proc. IJCAI","author":"Yang"},{"key":"ref102","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01246-5_41"},{"key":"ref103","doi-asserted-by":"publisher","DOI":"10.1039\/D1SC05180F"},{"key":"ref104","doi-asserted-by":"publisher","DOI":"10.1016\/j.dt.2022.02.007"},{"key":"ref105","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219890"},{"key":"ref106","article-title":"Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting","author":"Yu","year":"2017","journal-title":"arXiv:1709.04875"},{"key":"ref107","first-page":"11960","article-title":"Graph transformer networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Yun"},{"key":"ref108","doi-asserted-by":"publisher","DOI":"10.1002\/jcc.27490"},{"key":"ref109","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.668"},{"key":"ref110","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10735"},{"key":"ref111","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3151618"},{"key":"ref112","first-page":"1","article-title":"Link prediction based on graph neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zhang"},{"key":"ref113","article-title":"Hyper-SAGNN: A self-attention based graph neural network for hypergraphs","author":"Zhang","year":"2019","journal-title":"arXiv:1911.02613"},{"key":"ref114","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.31"},{"key":"ref115","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2935152"},{"key":"ref116","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2022.06.035"},{"key":"ref117","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2876865"},{"key":"ref118","first-page":"21834","article-title":"Dirichlet energy constrained learning for deep graph neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zhou"},{"key":"ref119","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2019.00050"},{"key":"ref120","doi-asserted-by":"publisher","DOI":"10.1186\/s13024-017-0218-4"},{"key":"ref121","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186116"},{"key":"ref122","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bty294"},{"key":"ref123","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2023.3238524"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10820123\/10960451.pdf?arnumber=10960451","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,16]],"date-time":"2025-04-16T17:52:16Z","timestamp":1744825936000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10960451\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":123,"URL":"https:\/\/doi.org\/10.1109\/access.2025.3558752","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}