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Databases (ECML PKDD), Turin, Italy, Sep. 18\u201322, 2023, pp. 683\u2013699. doi: 10.1007\/978-3-031-43415-0_40.","DOI":"10.1007\/978-3-031-43415-0_40"},{"key":"ref4","article-title":"DyTSCL: Dynamic graph representation via tempo-structural contrastive learning","volume":"556","author":"Li","year":"Nov. 2023","journal-title":"Neurocomputing"},{"key":"ref5","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2023.110144","article-title":"Dynamic graph contrastive learning via maximize temporal consistency","volume":"148","author":"Bao","year":"Apr. 2024","journal-title":"Pattern Recogn."},{"key":"ref6","doi-asserted-by":"crossref","first-page":"2765","DOI":"10.1109\/TKDE.2016.2591009","article-title":"Scalable temporal latent space inference for link prediction in dynamic social networks","volume":"28","author":"Zhu","year":"Jul. 2016","journal-title":"IEEE Trans. Know. Data. En."},{"key":"ref7","doi-asserted-by":"crossref","unstructured":"D. Rafailidis and A. Nanopoulos, \u201cModeling the dynamics of user preferences in coupled tensor factorization,\u201d presented at the ACM Conf. Recomm. Syst. (RecSys), Silicon Valley, CA, USA, Oct. 6\u201310, 2014, pp. 321\u2013324. doi: 10.1145\/2645710.2645758.","DOI":"10.1145\/2645710.2645758"},{"key":"ref8","doi-asserted-by":"crossref","unstructured":"S. D. Winter, T. Decuypere, S. Mitrovi\u0107, B. Baesens, and J. D. Weerdt, \u201cCombining temporal aspects of dynamic networks with Node2Vec for a more efficient dynamic link prediction,\u201d presented at the IEEE\/ACM Int. Conf. Adv. Soc. Netw. Anal. Min. (ASONAM), Barcelona, Spain, Aug. 28\u201331, 2018, pp. 1234\u20131241. doi: 10.1109\/asonam.2018.8508272.","DOI":"10.1109\/ASONAM.2018.8508272"},{"key":"ref9","doi-asserted-by":"crossref","unstructured":"S. Mahdavi, S. Khoshraftar, and A. An, \u201cdynnode2vec: Scalable dynamic network embeddingScalable dynamic network embedding,\u201d presented at the IEEE Int. Conf. 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