{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T18:22:44Z","timestamp":1788546164672,"version":"build-2803163510"},"reference-count":66,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"4","license":[{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Australian Research Council (ARC) Future Fellowship","award":["FT210100303"],"award-info":[{"award-number":["FT210100303"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1109\/tnnls.2024.3394161","type":"journal-article","created":{"date-parts":[[2024,5,10]],"date-time":"2024-05-10T17:26:27Z","timestamp":1715361987000},"page":"6080-6094","source":"Crossref","is-referenced-by-count":24,"title":["Scalable and Effective Temporal Graph Representation Learning With Hyperbolic Geometry"],"prefix":"10.1109","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7147-4498","authenticated-orcid":false,"given":"Yuanyuan","family":"Xu","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6572-2600","authenticated-orcid":false,"given":"Wenjie","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2273-1862","authenticated-orcid":false,"given":"Xiwei","family":"Xu","sequence":"additional","affiliation":[{"name":"Data61&#x2019;s Architecture and Analytics Platforms Team, CSIRO, Sydney, NSW, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8565-7287","authenticated-orcid":false,"given":"Binghao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Minerals and Energy Resources Engineering, University of New South Wales, Sydney, NSW, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2674-1638","authenticated-orcid":false,"given":"Ying","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Information Technology and the School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3084957"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3027426"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3222165"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330895"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3186103"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3220548"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3136171"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3178706"},{"key":"ref9","first-page":"70","article-title":"Representation learning for dynamic graphs: A survey","volume":"21","author":"Kazemi","year":"2020","journal-title":"J. Mach. Learn. Res."},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/3132847.3132919"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2822283"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.06.024"},{"key":"ref13","first-page":"10700","article-title":"Variational graph recurrent neural networks","volume-title":"Proc. Conf. Neural Inf. Process. Syst. (NIPS)","author":"Hajiramezanali"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3053652"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2996094"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467422"},{"key":"ref17","article-title":"Inductive representation learning on temporal graphs","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Xu"},{"key":"ref18","article-title":"Temporal graph networks for deep learning on dynamic graphs","volume-title":"Proc. ICML Workshop Graph Represent. Learn.","author":"Rossi"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3457564"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371845"},{"key":"ref21","article-title":"DyRep: Learning representations over dynamic graphs","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Trivedi"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/288"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557222"},{"key":"ref24","first-page":"19874","article-title":"Neural temporal walks: Motif-aware representation learning on continuous-time dynamic graphs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"35","author":"Jin"},{"key":"ref25","article-title":"Do we really need complicated model architectures for temporal networks?","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Cong"},{"key":"ref26","first-page":"1","article-title":"Neighborhood-aware scalable temporal network representation learning","volume-title":"Proc. Learn. Graphs Conf.","volume":"198","author":"Luo"},{"key":"ref27","first-page":"67686","article-title":"Towards better dynamic graph learning: New architecture and unified library","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"36","author":"Yu"},{"key":"ref28","first-page":"5350","article-title":"Hyperbolic neural networks","volume-title":"Proc. NIPS","author":"Ganea"},{"key":"ref29","first-page":"4869","article-title":"Hyperbolic graph convolutional neural networks","volume-title":"Proc. NeurIPS","author":"Chami"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.67.026112"},{"key":"ref31","first-page":"6338","article-title":"PoincaR\u00e9 embeddings for learning hierarchical representations","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Nickel"},{"key":"ref32","first-page":"8228","article-title":"Hyperbolic graph neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Liu"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1038\/nature06830"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1038\/nature11459"},{"key":"ref35","volume-title":"Riemannian Manifolds: An Introduction to Curvature","volume":"176","author":"Lee","year":"2006"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.82.036106"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref38","article-title":"Hyperbolic attention networks","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"G\u00fcl\u00e7ehre"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1142\/5914"},{"key":"ref40","first-page":"4592","article-title":"Riemannian SVRG: Fast stochastic optimization on Riemannian manifolds","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zhang"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2013.2254619"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1145\/3341301.3359646"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.14778\/3529337.3529342"},{"issue":"4","key":"ref44","first-page":"1","article-title":"GDELT: Global data on events, location, and tone, 1979\u20132012","volume-title":"Proc. ISA Annu. Conv.","volume":"2","author":"Leetaru"},{"key":"ref45","article-title":"OGB-LSC: A large-scale challenge for machine learning on graphs","author":"Hu","year":"2021","journal-title":"arXiv:2103.09430"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2013.77"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1002\/jgt.20275"},{"key":"ref48","article-title":"Graph attention networks","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Velickovic"},{"key":"ref49","volume-title":"TGL Library","year":"2022"},{"key":"ref50","volume-title":"Dynamic Graph Learning Library","year":"2023"},{"key":"ref51","volume-title":"Cytoscape","year":"2008"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467141"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3184723"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1145\/3439950"},{"key":"ref55","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1038\/nature11354"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/288"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11257"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3614899"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE60146.2024.00255"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1145\/3184558.3191526"},{"key":"ref62","article-title":"Inductive representation learning in temporal networks via causal anonymous walks","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Wang"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512164"},{"issue":"1","key":"ref64","first-page":"1","article-title":"Orca: Scalable temporal graph neural network training with theoretical guarantees","volume":"1","author":"Li","year":"2023","journal-title":"Proc. ACM Manage. Data"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00645"},{"key":"ref66","first-page":"4457","article-title":"Representation tradeoffs for hyperbolic embeddings","volume-title":"Proc. Int. Conf. Mach. Learn.","volume":"80","author":"Sala"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5962385\/10949581\/10528375.pdf?arnumber=10528375","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,7]],"date-time":"2025-04-07T21:55:47Z","timestamp":1744062947000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10528375\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4]]},"references-count":66,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2024.3394161","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4]]}}}