{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T16:35:31Z","timestamp":1781109331895,"version":"3.54.1"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,1,11]],"date-time":"2025-01-11T00:00:00Z","timestamp":1736553600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,11]],"date-time":"2025-01-11T00:00:00Z","timestamp":1736553600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100019091","name":"Key Research and Development Program of Hunan Province of China","doi-asserted-by":"publisher","award":["2023SK2038"],"award-info":[{"award-number":["2023SK2038"]}],"id":[{"id":"10.13039\/501100019091","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100019091","name":"Key Research and Development Program of Hunan Province of China","doi-asserted-by":"publisher","award":["2023SK2038"],"award-info":[{"award-number":["2023SK2038"]}],"id":[{"id":"10.13039\/501100019091","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100019091","name":"Key Research and Development Program of Hunan Province of China","doi-asserted-by":"publisher","award":["2023SK2038"],"award-info":[{"award-number":["2023SK2038"]}],"id":[{"id":"10.13039\/501100019091","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"DOI":"10.1007\/s11227-024-06881-5","type":"journal-article","created":{"date-parts":[[2025,1,11]],"date-time":"2025-01-11T10:38:34Z","timestamp":1736591914000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Continuous-time dynamic graph learning based on spatio-temporal random walks"],"prefix":"10.1007","volume":"81","author":[{"given":"Jinfang","family":"Sheng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,1,11]]},"reference":[{"issue":"70","key":"6881_CR1","first-page":"1","volume":"21","author":"SM Kazemi","year":"2020","unstructured":"Kazemi SM, Goel R, Jain K, Kobyzev I, Sethi A, Forsyth P, Poupart P (2020) Representation learning for dynamic graphs: a survey. J Mach Learn Res 21(70):1\u201373","journal-title":"J Mach Learn Res"},{"issue":"5","key":"6881_CR2","doi-asserted-by":"publisher","first-page":"586","DOI":"10.1038\/s41562-020-01024-1","volume":"5","author":"U Alvarez-Rodriguez","year":"2021","unstructured":"Alvarez-Rodriguez U, Battiston F, Arruda GF, Moreno Y, Perc M, Latora V (2021) Evolutionary dynamics of higher-order interactions in social networks. Nat Hum Behav 5(5):586\u2013595","journal-title":"Nat Hum Behav"},{"key":"6881_CR3","doi-asserted-by":"publisher","first-page":"1475","DOI":"10.1109\/TKDE.2023.3309982","volume":"36","author":"L Yu","year":"2023","unstructured":"Yu L, Liu Z, Sun L, Du B, Liu C, Lv W (2023) Continuous-time user preference modelling for temporal sets prediction. IEEE Trans Knowl Data Eng 36:1475\u20131488","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"12","key":"6881_CR4","doi-asserted-by":"publisher","first-page":"23680","DOI":"10.1109\/TITS.2022.3208943","volume":"23","author":"Y Sun","year":"2022","unstructured":"Sun Y, Jiang X, Hu Y, Duan F, Guo K, Wang B, Gao J, Yin B (2022) Dual dynamic spatial-temporal graph convolution network for traffic prediction. IEEE Trans Intell Transp Syst 23(12):23680\u201323693","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"6881_CR5","volume-title":"The sociology of Georg Simmel","author":"G Simmel","year":"1950","unstructured":"Simmel G (1950) The sociology of Georg Simmel, vol 92892. Simon and Schuster, New York"},{"issue":"6","key":"6881_CR6","doi-asserted-by":"publisher","first-page":"1360","DOI":"10.1086\/225469","volume":"78","author":"MS Granovetter","year":"1973","unstructured":"Granovetter MS (1973) The strength of weak ties. Am J Sociol 78(6):1360\u20131380","journal-title":"Am J Sociol"},{"key":"6881_CR7","first-page":"5363","volume":"34","author":"A Pareja","year":"2020","unstructured":"Pareja A, Domeniconi G, Chen J, Ma T, Suzumura T, Kanezashi H, Kaler T, Schardl T, Leiserson C (2020) EvolveGCN: evolving graph convolutional networks for dynamic graphs. Proc AAAI Conf Artif Intell 34:5363\u20135370","journal-title":"Proc AAAI Conf Artif Intell"},{"issue":"9","key":"6881_CR8","doi-asserted-by":"publisher","first-page":"3848","DOI":"10.1109\/TITS.2019.2935152","volume":"21","author":"L Zhao","year":"2019","unstructured":"Zhao L, Song Y, Zhang C, Liu Y, Wang P, Lin T, Deng M, Li H (2019) T-GCN: a temporal graph convolutional network for traffic prediction. IEEE Trans Intell Transp Syst 21(9):3848\u20133858","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"6881_CR9","doi-asserted-by":"crossref","unstructured":"Seo Y, Defferrard M, Vandergheynst P, Bresson X (2018) Structured sequence modeling with graph convolutional recurrent networks. In: Neural Information Processing: 25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13-16, 2018, Proceedings, Part I 25. Springer, pp 362\u2013373","DOI":"10.1007\/978-3-030-04167-0_33"},{"key":"6881_CR10","doi-asserted-by":"crossref","unstructured":"Wang J, Zhu W, Song G, Wang L (2022) Streaming graph neural networks with generative replay. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp 1878\u20131888","DOI":"10.1145\/3534678.3539336"},{"key":"6881_CR11","unstructured":"Goyal P, Kamra N, He X, Liu Y (2018). DynGEM: deep embedding method for dynamic graphs. arXiv preprint arXiv:1805.11273"},{"key":"6881_CR12","doi-asserted-by":"crossref","unstructured":"Sankar A, Wu Y, Gou L, Zhang W, Yang H (2020). DySAT: Deep neural representation learning on dynamic graphs via self-attention networks. In: Proceedings of the 13th International Conference on Web Search and Data Mining, pp 519\u2013527","DOI":"10.1145\/3336191.3371845"},{"key":"6881_CR13","unstructured":"Xu D, Ruan C, Korpeoglu E, Kumar S, Achan K (2020) Inductive representation learning on temporal graphs. In: International Conference on Learning Representations"},{"key":"6881_CR14","unstructured":"Rossi E, Chamberlain B, Frasca F, Eynard D, Monti F, Bronstein M (2020) Temporal graph networks for deep learning on dynamic graphs. arXiv preprint arXiv:2006.10637"},{"key":"6881_CR15","unstructured":"Wang Y, Chang YY, Liu Y, Leskovec J, Li P (2021) Inductive representation learning in temporal networks via causal anonymous walks. In: International Conference on Learning Representations (ICLR)"},{"key":"6881_CR16","first-page":"32257","volume":"35","author":"A Souza","year":"2022","unstructured":"Souza A, Mesquita D, Kaski S, Garg V (2022) Provably expressive temporal graph networks. Adv Neural Inf Process Syst 35:32257\u201332269","journal-title":"Adv Neural Inf Process Syst"},{"key":"6881_CR17","unstructured":"Cong W, Zhang S, Kang J, Yuan B, Wu H, Zhou X, Tong H, Mahdavi M (2023) Do we really need complicated model architectures for temporal networks? In: The Eleventh International Conference on Learning Representations"},{"key":"6881_CR18","unstructured":"Trivedi R, Farajtabar M, Biswal P, Zha H (2019) DyRep: Learning representations over dynamic graphs. In: International Conference on Learning Representations"},{"key":"6881_CR19","unstructured":"Wang L, Chang X, Li S, Chu Y, Li H, Zhang W, He X, Song L, Zhou J, Yang H (2021) TCL: transformer-based dynamic graph modelling via contrastive learning. arXiv preprint arXiv:2105.07944"},{"key":"6881_CR20","doi-asserted-by":"crossref","unstructured":"Kumar S, Zhang X, Leskovec J (2019). Predicting dynamic embedding trajectory in temporal interaction networks. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 1269\u20131278","DOI":"10.1145\/3292500.3330895"},{"key":"6881_CR21","doi-asserted-by":"crossref","unstructured":"Wang X, Lyu D, Li M, Xia Y, Yang Q, Wang X, Wang X, Cui P, Yang Y, Sun B, et al (2021) APAN: Asynchronous propagation attention network for real-time temporal graph embedding. In: Proceedings of the 2021 International Conference on Management of Data, pp 2628\u20132638","DOI":"10.1145\/3448016.3457564"},{"key":"6881_CR22","doi-asserted-by":"crossref","unstructured":"Nguyen GH, Lee JB, Rossi RA, Ahmed NK, Koh E, Kim S (2018) Dynamic network embeddings: from random walks to temporal random walks. In: 2018 IEEE International Conference on Big Data (Big Data), IEEE, pp 1085\u20131092","DOI":"10.1109\/BigData.2018.8622109"},{"issue":"8","key":"6881_CR23","doi-asserted-by":"publisher","first-page":"2867","DOI":"10.1007\/s13042-023-01803-y","volume":"14","author":"M Zhang","year":"2023","unstructured":"Zhang M, Xu B, Wang L (2023) Dynamic network link prediction based on random walking and time aggregation. Int J Mach Learn Cybern 14(8):2867\u20132875","journal-title":"Int J Mach Learn Cybern"},{"key":"6881_CR24","first-page":"5998","volume":"30","author":"A Vaswani","year":"2017","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. Adv Neural Inf Processing Syst 30:5998\u20136008","journal-title":"Adv Neural Inf Processing Syst"},{"key":"6881_CR25","unstructured":"Feng Z, Wang R, Wang T, Song M, Wu S, He S (2024) A comprehensive survey of dynamic graph neural networks: Models, frameworks, benchmarks, experiments and challenges. arXiv preprint arXiv:2405.00476"},{"key":"6881_CR26","doi-asserted-by":"publisher","first-page":"43460","DOI":"10.1109\/ACCESS.2024.3378111","volume":"12","author":"L Yang","year":"2024","unstructured":"Yang L, Chatelain C, Adam S (2024) Dynamic graph representation learning with neural networks: a survey. IEEE Access 12:43460\u201343484","journal-title":"IEEE Access"},{"key":"6881_CR27","unstructured":"Trivedi R, Dai H, Wang Y, Song L (2017) Know-evolve: deep temporal reasoning for dynamic knowledge graphs. In: International Conference on Machine Learning, PMLR, pp 3462\u20133471"},{"key":"6881_CR28","unstructured":"Mikolov T, Chen K, Corrado G, Dean J (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781"},{"key":"6881_CR29","unstructured":"Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems 26"},{"key":"6881_CR30","doi-asserted-by":"crossref","unstructured":"Perozzi B, Al-Rfou R, Skiena S (2014) DeepWalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 701\u2013710","DOI":"10.1145\/2623330.2623732"},{"key":"6881_CR31","doi-asserted-by":"crossref","unstructured":"Grover A, Leskovec J (2016) node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 855\u2013864","DOI":"10.1145\/2939672.2939754"},{"issue":"1","key":"6881_CR32","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1186\/s13677-023-00460-4","volume":"12","author":"Z Liu","year":"2023","unstructured":"Liu Z, Che W, Wang S, Xu J, Yin H (2023) A large-scale data security detection method based on continuous time graph embedding framework. J Cloud Comput 12(1):89","journal-title":"J Cloud Comput"},{"key":"6881_CR33","unstructured":"Veli\u010dkovi\u0107 P, Cucurull G, Casanova A, Romero A, Li\u00f2 P, Bengio Y (2018) Graph attention networks. In: International Conference on Learning Representations"},{"key":"6881_CR34","doi-asserted-by":"crossref","unstructured":"Wang X, Ji H, Shi C, Wang B, Ye Y, Cui P, Yu PS (2019) Heterogeneous graph attention network. In: The World Wide Web Conference, pp 2022\u20132032","DOI":"10.1145\/3308558.3313562"},{"key":"6881_CR35","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1016\/j.future.2019.05.033","volume":"100","author":"Y Zhang","year":"2019","unstructured":"Zhang Y, Shi Z, Feng D, Zhan X-X (2019) Degree-biased random walk for large-scale network embedding. Futur Gener Comput Syst 100:198\u2013209","journal-title":"Futur Gener Comput Syst"},{"key":"6881_CR36","first-page":"19874","volume":"35","author":"M Jin","year":"2022","unstructured":"Jin M, Li Y-F, Pan S (2022) Neural temporal walks: Motif-aware representation learning on continuous-time dynamic graphs. Adv Neural Inf Process Syst 35:19874\u201319886","journal-title":"Adv Neural Inf Process Syst"},{"key":"6881_CR37","first-page":"32928","volume":"35","author":"F Poursafaei","year":"2022","unstructured":"Poursafaei F, Huang S, Pelrine K, Rabbany R (2022) Towards better evaluation for dynamic link prediction. Adv Neural Inf Process Syst 35:32928\u201332941","journal-title":"Adv Neural Inf Process Syst"},{"key":"6881_CR38","first-page":"67686","volume":"36","author":"L Yu","year":"2023","unstructured":"Yu L, Sun L, Du B, Lv W (2023) Towards better dynamic graph learning: new architecture and unified library. Adv Neural Inf Process Syst 36:67686\u201367700","journal-title":"Adv Neural Inf Process Syst"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-024-06881-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-024-06881-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-024-06881-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,11]],"date-time":"2025-01-11T11:03:55Z","timestamp":1736593435000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-024-06881-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,11]]},"references-count":38,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,1]]}},"alternative-id":["6881"],"URL":"https:\/\/doi.org\/10.1007\/s11227-024-06881-5","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,11]]},"assertion":[{"value":"21 December 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 January 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"389"}}