{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T19:03:26Z","timestamp":1763665406420,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":33,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,3,4]],"date-time":"2024-03-04T00:00:00Z","timestamp":1709510400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2020YFC0833302"],"award-info":[{"award-number":["2020YFC0833302"]}]},{"name":"National Natural Science Foundation of China","award":["62076059"],"award-info":[{"award-number":["62076059"]}]},{"name":"Science Project of Liaoning province","award":["2021-MS-105"],"award-info":[{"award-number":["2021-MS-105"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,3,4]]},"DOI":"10.1145\/3616855.3635765","type":"proceedings-article","created":{"date-parts":[[2024,3,4]],"date-time":"2024-03-04T18:18:12Z","timestamp":1709576292000},"page":"443-451","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Capturing Temporal Node Evolution via Self-supervised Learning: A New Perspective on Dynamic Graph Learning"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3525-2297","authenticated-orcid":false,"given":"Lingwen","family":"Liu","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6786-6261","authenticated-orcid":false,"given":"Guangqi","family":"Wen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7859-2769","authenticated-orcid":false,"given":"Peng","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7754-1273","authenticated-orcid":false,"given":"Jinzhu","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2958-3097","authenticated-orcid":false,"given":"Weiping","family":"Li","sequence":"additional","affiliation":[{"name":"School of Software and Microelectronics, Peking University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0060-5988","authenticated-orcid":false,"given":"Osmar R.","family":"Zaiane","sequence":"additional","affiliation":[{"name":"Alberta Machine Intelligence Institute, University of Alberta, Edmonton, Alberta, Canada"}]}],"member":"320","published-online":{"date-parts":[[2024,3,4]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2022.01.048"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219986"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2022.05.070"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330925"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/285"},{"key":"e_1_3_2_1_6_1","volume-title":"Advances in Neural Information Processing Systems","author":"Hamilton Will","year":"2017","unstructured":"Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In Advances in Neural Information Processing Systems, I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.), Vol. 30. Curran Associates, Inc., Long Beach, CA, USA. https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2017\/file\/5dd9db5e033da9c6fb5ba83c7a7ebea9-Paper.pdf"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3109859.3109882"},{"key":"e_1_3_2_1_8_1","volume-title":"Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, Yuji Matsumoto and Rashmi Prasad (Eds.). The COLING 2016 Organizing Committee","author":"Jiang Tingsong","year":"2016","unstructured":"Tingsong Jiang, Tianyu Liu, Tao Ge, Lei Sha, Baobao Chang, Sujian Li, and Zhifang Sui. 2016a. Towards Time-Aware Knowledge Graph Completion. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, Yuji Matsumoto and Rashmi Prasad (Eds.). The COLING 2016 Organizing Committee, Osaka, Japan, 1715--1724. https:\/\/aclanthology.org\/C16--1161"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D16-1260"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401072"},{"volume-title":"Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations. arXiv, Palais des Congr\u00e8s Neptune","author":"Thomas","key":"e_1_3_2_1_11_1","unstructured":"Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations. arXiv, Palais des Congr\u00e8s Neptune, Toulon, France, 1--14."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186141"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3159652.3159729"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2016.0033"},{"key":"e_1_3_2_1_15_1","volume-title":"Dynamic graph convolutional recurrent network for traffic prediction: Benchmark and solution. ACM Transactions on Knowledge Discovery from Data","author":"Li Fuxian","year":"2023","unstructured":"Fuxian Li, Jie Feng, Huan Yan, Guangyin Jin, Fan Yang, Funing Sun, Depeng Jin, and Yong Li. 2023. Dynamic graph convolutional recurrent network for traffic prediction: Benchmark and solution. ACM Transactions on Knowledge Discovery from Data, Vol. 17, 1 (2023), 1--21."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5911"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441741"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2022.933660"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3145092"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106521"},{"volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"Mahdavi Sedigheh","key":"e_1_3_2_1_21_1","unstructured":"Sedigheh Mahdavi, Shima Khoshraftar, and Aijun An. 2020. Dynamic Joint Variational Graph Autoencoders. In Machine Learning and Knowledge Discovery in Databases, Peggy Cellier and Kurt Driessens (Eds.). Springer International Publishing, Cham, 385--401."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v30i1.10314"},{"key":"e_1_3_2_1_23_1","volume-title":"AAAI 2022 Workshop on Scientific Document Understanding (SDU","author":"Holm Andreas Nugaard","year":"2022","unstructured":"Andreas Nugaard Holm, Barbara Plank, Dustin Wright, and Isabelle Augenstein. 2022. Longitudinal Citation Prediction using Temporal Graph Neural Networks. In AAAI 2022 Workshop on Scientific Document Understanding (SDU 2022). AAAI Press, United States."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.5555\/1543767.1543769"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5984"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371845"},{"volume-title":"Structured Sequence Modeling with Graph Convolutional Recurrent Networks","author":"Seo Youngjoo","key":"e_1_3_2_1_27_1","unstructured":"Youngjoo Seo, Micha\u00ebl Defferrard, Pierre Vandergheynst, and Xavier Bresson. 2018. Structured Sequence Modeling with Graph Convolutional Recurrent Networks. In Neural Information Processing, Long Cheng, Andrew Chi Sing Leung, and Seiichi Ozawa (Eds.). Springer International Publishing, Cham, 362--373."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482389"},{"key":"e_1_3_2_1_29_1","volume-title":"Graph attention networks. stat","author":"Velickovic Petar","year":"2017","unstructured":"Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. stat , Vol. 1050 (2017), 20."},{"key":"e_1_3_2_1_30_1","volume-title":"International Conference on Learning Representations. Association for Computing Machinery","author":"Wang Yanbang","year":"2021","unstructured":"Yanbang Wang, Yen-Yu Chang, Yunyu Liu, Jure Leskovec, and Pan Li. 2021. Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks. In International Conference on Learning Representations. Association for Computing Machinery, New York, NY, USA."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539300"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109234"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2935152"}],"event":{"name":"WSDM '24: The 17th ACM International Conference on Web Search and Data Mining","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data","SIGIR ACM Special Interest Group on Information Retrieval"],"location":"Merida Mexico","acronym":"WSDM '24"},"container-title":["Proceedings of the 17th ACM International Conference on Web Search and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3616855.3635765","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3616855.3635765","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T00:50:14Z","timestamp":1755823814000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3616855.3635765"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,4]]},"references-count":33,"alternative-id":["10.1145\/3616855.3635765","10.1145\/3616855"],"URL":"https:\/\/doi.org\/10.1145\/3616855.3635765","relation":{},"subject":[],"published":{"date-parts":[[2024,3,4]]},"assertion":[{"value":"2024-03-04","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}