{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T16:18:20Z","timestamp":1783095500175,"version":"3.54.6"},"reference-count":73,"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":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2020AAA0107100"],"award-info":[{"award-number":["2020AAA0107100"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62325604"],"award-info":[{"award-number":["62325604"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62276271"],"award-info":[{"award-number":["62276271"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Public Service Platform for Artificial Intelligence Screening and Auxiliary Diagnosis for the Medical and Health Industry, Ministry of Industry and Information Technology of the People\u2019s Taiwan","award":["2020-0103-3-1"],"award-info":[{"award-number":["2020-0103-3-1"]}]}],"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.2024.3386168","type":"journal-article","created":{"date-parts":[[2024,4,22]],"date-time":"2024-04-22T13:40:16Z","timestamp":1713793216000},"page":"6355-6367","source":"Crossref","is-referenced-by-count":62,"title":["Self-Supervised Temporal Graph Learning With Temporal and Structural Intensity Alignment"],"prefix":"10.1109","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3900-4204","authenticated-orcid":false,"given":"Meng","family":"Liu","sequence":"first","affiliation":[{"name":"School of Computer, National University of Defense Technology, Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ke","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Computer, National University of Defense Technology, Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8352-0092","authenticated-orcid":false,"given":"Yawei","family":"Zhao","sequence":"additional","affiliation":[{"name":"Medical Big Data Research Center, Chinese PLA General Hospital, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1353-2968","authenticated-orcid":false,"given":"Wenxuan","family":"Tu","sequence":"additional","affiliation":[{"name":"School of Computer, National University of Defense Technology, Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1491-4594","authenticated-orcid":false,"given":"Sihang","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinbiao","family":"Gan","sequence":"additional","affiliation":[{"name":"School of Computer, National University of Defense Technology, Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9066-1475","authenticated-orcid":false,"given":"Xinwang","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer, National University of Defense Technology, Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3335-5700","authenticated-orcid":false,"given":"Kunlun","family":"He","sequence":"additional","affiliation":[{"name":"Medical Big Data Research Center, Chinese PLA General Hospital, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2849727"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/3159652.3159706"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331267"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3184970"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939751"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3055147"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467421"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380214"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.26599\/TST.2021.9010090"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1971.tb01530.x"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3164982"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbad216"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3591711"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3070843"},{"key":"ref16","article-title":"SARF: Aliasing relation assisted self-supervised learning for few-shot relation reasoning","author":"Meng","year":"2023","journal-title":"arXiv:2304.10297"},{"key":"ref17","article-title":"Spatiotemporal graph neural networks with uncertainty quantification for traffic incident risk prediction","author":"Gao","year":"2023","journal-title":"arXiv:2309.05072"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26285"},{"key":"ref19","article-title":"A survey of knowledge graph reasoning on graph types: Static, dynamic, and multimodal","author":"Liang","year":"2022","journal-title":"arXiv:2212.05767"},{"key":"ref20","article-title":"Structure guided multi-modal pre-trained transformer for knowledge graph reasoning","author":"Liang","year":"2023","journal-title":"arXiv:2307.03591"},{"key":"ref21","first-page":"24983","article-title":"Disentangled multiplex graph representation learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Mo"},{"key":"ref22","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.105946","article-title":"Multi-view underwater image enhancement method via embedded fusion mechanism","volume":"121","author":"Zhou","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3282989"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3268069"},{"key":"ref25","article-title":"G2uardFL: Safeguarding federated learning against backdoor attacks through attributed client graph clustering","author":"Yu","year":"2023","journal-title":"arXiv:2306.04984"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313562"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3178156"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3323624"},{"issue":"4","key":"ref29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3485189","article-title":"Scalable representation learning for dynamic heterogeneous information networks via metagraphs","volume":"40","author":"Fang","year":"2022","journal-title":"ACM Trans. Inf. Syst."},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3172588"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3155478"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i8.26201"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3191086"},{"key":"ref34","first-page":"319","article-title":"CONVERT: Contrastive graph clustering with reliable augmentation","volume-title":"Proc. 31st ACM Int. Conf. Multimedia","author":"Yang"},{"key":"ref35","first-page":"701","article-title":"DeepWalk: Online learning of social representations","volume-title":"Proc. 20th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining","author":"Perozzi"},{"key":"ref36","first-page":"855","article-title":"node2vec: Scalable feature learning for networks","volume-title":"Proc. 22nd ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining","author":"Grover"},{"key":"ref37","article-title":"Variational graph auto-encoders","volume-title":"Proc. NeurIPS","author":"Kipf"},{"key":"ref38","article-title":"Inductive representation learning on large graphs","volume-title":"Proc. NeurIPS","volume":"30","author":"Hamilton"},{"key":"ref39","first-page":"19620","article-title":"Parameterized explainer for graph neural network","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Luo"},{"key":"ref40","first-page":"30414","article-title":"InfoGCL: Information-aware graph contrastive learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Xu"},{"key":"ref41","first-page":"330","article-title":"Node classification in temporal graphs through stochastic sparsification and temporal structural convolution","volume-title":"Proc. ECML PKDD","author":"Zheng"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3185527"},{"key":"ref43","article-title":"Deep temporal graph clustering","author":"Liu","year":"2023","journal-title":"arXiv:2305.10738"},{"issue":"4","key":"ref44","first-page":"5363","article-title":"EvolveGCN: Evolving graph convolutional networks for dynamic graphs","volume-title":"Proc. AAAI","volume":"34","author":"Pareja"},{"key":"ref45","first-page":"519","article-title":"DySAT: Deep neural representation learning on dynamic graphs via self-attention networks","volume-title":"Proc. 13th Int. Conf. Web Search Data Mining","author":"Sankar"},{"key":"ref46","first-page":"969","article-title":"Continuous-time dynamic network embeddings","volume-title":"Proc. Companion The Web Conf.","author":"Nguyen"},{"key":"ref47","first-page":"2857","article-title":"Embedding temporal network via neighborhood formation","volume-title":"Proc. 24th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining","author":"Zuo"},{"key":"ref48","first-page":"469","article-title":"Temporal network embedding with micro- and macro-dynamics","volume-title":"Proc. 28th ACM Int. Conf. Inf. Knowl. Manage.","author":"Lu"},{"key":"ref49","first-page":"3947","article-title":"Spatio-temporal attentive RNN for node classification in temporal attributed graphs","volume-title":"Proc. 28th Int. Joint Conf. Artif. Intell.","author":"Xu"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2019.00181"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2002.07962"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3463052"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16583"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512164"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3178706"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3136171"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3257488"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1145\/3581783.3611853"},{"key":"ref59","article-title":"DyExplainer: Explainable dynamic graph neural networks","author":"Wang","year":"2023","journal-title":"arXiv:2310.16375"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2019.102142"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-021-03102-x"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1086\/226707"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2807843"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.3724\/SP.J.1016.2011.01956"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11671"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219986"},{"key":"ref67","article-title":"Distributed representations of words and phrases and their compositionality","volume-title":"Proc. NeurIPS","volume":"26","author":"Mikolov"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330895"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1002\/asi.21015"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1145\/1081870.1081893"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1145\/3159652.3159729"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1018"},{"key":"ref73","article-title":"Adam: A method for stochastic optimization","volume-title":"Proc. ICLR","author":"Kingma"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5962385\/10949581\/10506201.pdf?arnumber=10506201","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T18:39:03Z","timestamp":1764959943000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10506201\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4]]},"references-count":73,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2024.3386168","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4]]}}}