{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T20:46:03Z","timestamp":1778359563481,"version":"3.51.4"},"reference-count":92,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"9","license":[{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"European Union (EU) NextGenerationEU Programme","award":["PNRR-PE-AI FAIR"],"award-info":[{"award-number":["PNRR-PE-AI FAIR"]}]},{"name":"EU H2020 TAILOR Project","award":["952215"],"award-info":[{"award-number":["952215"]}]},{"name":"HE EIC EMERGE","award":["101070918"],"award-info":[{"award-number":["101070918"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2024,9]]},"DOI":"10.1109\/tnnls.2024.3379735","type":"journal-article","created":{"date-parts":[[2024,4,3]],"date-time":"2024-04-03T17:47:22Z","timestamp":1712166442000},"page":"11788-11801","source":"Crossref","is-referenced-by-count":21,"title":["Deep Learning for Dynamic Graphs: Models and Benchmarks"],"prefix":"10.1109","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5526-2479","authenticated-orcid":false,"given":"Alessio","family":"Gravina","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Pisa, Pisa, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5213-2468","authenticated-orcid":false,"given":"Davide","family":"Bacciu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Pisa, Pisa, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1263","article-title":"Neural message passing for quantum chemistry","volume-title":"Proc. 34th ICML","volume":"70","author":"Gilmer"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bty294"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1009531"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TETC.2023.3238963"},{"key":"ref5","article-title":"Fake news detection on social media using geometric deep learning","author":"Monti","year":"2019","journal-title":"arXiv:1902.06673"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3481916"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2020.06.006"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2935152"},{"key":"ref10","first-page":"1","article-title":"Temporal graph networks for deep learning on dynamic graphs","volume-title":"Proc. ICML","author":"Rossi"},{"key":"ref11","first-page":"1","article-title":"DyRep: Learning representations over dynamic graphs","volume-title":"Proc. ICLR","author":"Trivedi"},{"key":"ref12","first-page":"1","article-title":"Inductive representation learning on temporal graphs","volume-title":"Proc. ICLR","author":"Xu"},{"issue":"1","key":"ref13","first-page":"1","article-title":"Representation learning for dynamic graphs: A survey","volume":"21","author":"Kazemi","year":"2020","journal-title":"J. Mach. Learn. Res."},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.117921"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-349-03521-2"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2008.2005605"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2008.2010350"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1606.09375"},{"key":"ref19","first-page":"1","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"Proc. ICLR","author":"Kipf"},{"key":"ref20","first-page":"1","article-title":"Graph attention networks","volume-title":"Proc. ICLR","author":"Velickovic"},{"key":"ref21","first-page":"1","article-title":"Inductive representation learning on large graphs","volume-title":"Proc. NIPS","author":"Hamilton"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330925"},{"key":"ref23","first-page":"1","article-title":"How powerful are graph neural networks?","volume-title":"Proc. ICLR","author":"Xu"},{"issue":"9","key":"ref24","first-page":"12","article-title":"A reduction of a graph to a canonical form and an algebra arising during this reduction","volume":"2","author":"Weisfeiler","year":"1968","journal-title":"Nauchno-Technicheskaya Informatsia"},{"key":"ref25","first-page":"1","article-title":"Anti-symmetric DGN: A stable architecture for deep graph networks","volume-title":"Proc. ICLR","author":"Gravina"},{"key":"ref26","first-page":"5758","article-title":"Dissecting the diffusion process in linear graph convolutional networks","volume-title":"Proc. NIPS","author":"Wang"},{"key":"ref27","first-page":"6861","article-title":"Simplifying graph convolutional networks","volume-title":"Proc. 36th ICML","volume":"97","author":"Wu"},{"key":"ref28","first-page":"3836","article-title":"PDE-GCN: Novel architectures for graph neural networks motivated by partial differential equations","volume-title":"Proc. NIPS","author":"Eliasof"},{"key":"ref29","article-title":"Graph-coupled oscillator networks","author":"Konstantin Rusch","year":"2022","journal-title":"arXiv:2202.02296"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"ref31","first-page":"1","article-title":"Efficient estimation of word representations in vector space","volume-title":"Proc. ICLR","author":"Mikolov"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/s10044-008-0141-y"},{"key":"ref34","first-page":"1","article-title":"Graph edit networks","volume-title":"Proc. ICLR","author":"Paassen"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1038\/323533a0"},{"key":"ref36","first-page":"362","article-title":"Structured peepholeequence modeling with graph convolutional recurrent networks","volume-title":"Proc. ICONIP","author":"Seo"},{"issue":"1","key":"ref37","first-page":"115","article-title":"Learning precise timing with LSTM recurrent networks","volume":"3","author":"Gers","year":"2002","journal-title":"J. Mach. Learn. Res."},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.31390\/gradschool_dissertations.4601"},{"key":"ref39","first-page":"1","article-title":"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting","volume-title":"Proc. ICLR","author":"Li"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1406.1078"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.3390\/ijgi10070485"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/505"},{"key":"ref43","first-page":"933","article-title":"Language modeling with gated convolutional networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Dauphin"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301922"},{"key":"ref45","article-title":"GC-LSTM: Graph convolution embedded LSTM for dynamic link prediction","author":"Chen","year":"2018","journal-title":"arXiv:1812.04206"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330847"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-93417-4_38"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2022.05.001"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2010.5596796"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i6.16616"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539300"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330919"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i6.25880"},{"issue":"34","key":"ref54","article-title":"The \u2018echo state\u2019 approach to analysing and training recurrent neural networks\u2014With an erratum note","volume":"148","author":"Jaeger","year":"2010"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1126\/science.1091277"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-43883-8_3"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5984"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1145\/3341161.3342872"},{"key":"ref59","first-page":"1","article-title":"Gated graph sequence neural networks","volume-title":"Proc. ICLR","author":"Li"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.06.024"},{"key":"ref61","article-title":"DynGEM: Deep embedding method for dynamic graphs","author":"Goyal","year":"2018","journal-title":"arXiv:1805.11273"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-05710-7_37"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330895"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401092"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1145\/3184558.3191526"},{"key":"ref66","first-page":"1","article-title":"Inductive representation learning in temporal networks via causal anonymous walks","volume-title":"Proc. ICLR","author":"Wang"},{"key":"ref67","first-page":"19874","article-title":"Neural temporal walks: Motif-aware representation learning on continuous-time dynamic graphs","volume-title":"Proc. NIPS","author":"Jin"},{"key":"ref68","first-page":"32257","article-title":"Provably expressive temporal graph networks","volume-title":"Proc. NIPS","author":"Souza"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.21105\/joss.05713"},{"key":"ref70","first-page":"1","article-title":"Fast graph representation learning with PyTorch geometric","volume-title":"Proc. ICLR","author":"Fey"},{"key":"ref71","volume-title":"SNAP Datasets: Stanford Large Network Dataset Collection","author":"Leskovec","year":"2014"},{"key":"ref72","volume-title":"Torch Spatiotemporal","author":"Cini","year":"2022"},{"key":"ref73","first-page":"1","article-title":"The network data repository with interactive graph analytics and visualization","volume-title":"Proc. AAAI","author":"Rossi"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482014"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1007\/s41109-018-0080-5"},{"key":"ref76","first-page":"1","article-title":"Anti-money laundering in Bitcoin: Experimenting with graph convolutional networks for financial forensics","volume-title":"Proc. Workshop Anomaly Detection Finance","author":"Weber"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1145\/1081870.1081893"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2016.0033"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1145\/3159652.3159729"},{"key":"ref80","first-page":"32928","article-title":"Towards better evaluation for dynamic link prediction","volume-title":"Proc. NIPS","author":"Poursafaei"},{"key":"ref81","first-page":"1","article-title":"Geom-GCN: Geometric graph convolutional networks","volume-title":"Proc. ICLR","author":"Pei"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM54844.2022.00169"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN54540.2023.10191196"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3070843"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3055147"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-12515-7_1"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3136171"},{"key":"ref88","article-title":"A note on over-smoothing for graph neural networks","author":"Cai","year":"2020","journal-title":"arXiv:2006.13318"},{"key":"ref89","first-page":"1","article-title":"On the bottleneck of graph neural networks and its practical implications","volume-title":"Proc. ICLR","author":"Alon"},{"key":"ref90","article-title":"On over-squashing in message passing neural networks: The impact of width, depth, and topology","author":"Di Giovanni","year":"2023","journal-title":"arXiv:2302.02941"},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-16-6054-2_5"},{"key":"ref92","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3172588"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5962385\/10663876\/10490120.pdf?arnumber=10490120","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,11]],"date-time":"2024-12-11T01:51:01Z","timestamp":1733881861000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10490120\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9]]},"references-count":92,"journal-issue":{"issue":"9"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2024.3379735","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9]]}}}