Page 202 - 《软件学报》2026年第1期
P. 202

刘子扬 等: 图对比学习方法综述                                                                 199


                 [80]   Jiao  YZ,  Xiong  Y,  Zhang  JW,  Zhang  Y,  Zhang  TQ,  Zhu  YY.  Sub-graph  contrast  for  scalable  self-supervised  graph  representation
                     learning. In: Proc. of the 2020 IEEE Int’l Conf. on Data Mining. Sorrento: IEEE, 2020. 222–231. [doi: 10.1109/ICDM50108.2020.00031]
                 [81]   Bielak P, Kajdanowicz T, Chawla NV. Graph Barlow twins: A self-supervised representation learning framework for graphs. Knowledge-
                     based Systems, 2022, 256: 109631. [doi: 10.1016/j.knosys.2022.109631]
                 [82]   Li JC, Lu GQ, Li JC. A self-supervised graph autoencoder with Barlow twins. In: Proc. of the 19th Pacific Rim Int’l Conf. on Artificial
                     Intelligence. Shanghai: Springer, 2022. 501–512. [doi: 10.1007/978-3-031-20865-2_37]
                 [83]   Jin M, Zheng YZ, Li YF, Gong C, Zhou C, Pan SR. Multi-scale contrastive siamese networks for self-supervised graph representation
                     learning. In: Proc. of the 13th Int’l Joint Conf. on Artificial Intelligence. 2021. 1477–1483. [doi: 10.24963/ijcai.2021/204]
                 [84]   Zheng YZ, Pan SR, Lee VCS, Zheng Y, Yu PS. Rethinking and scaling up graph contrastive learning: An extremely efficient approach
                     with group discrimination. In: Proc. of the 36th Int’l Conf. on Neural Information Processing Systems. New Orleans: Curran Associates
                     Inc., 2022. 10809–10820.
                 [85]   Chen D, Zhao X, Wang W, Tan Z, Xiao WD. Graph self-supervised learning with augmentation-aware contrastive learning. In: Proc. of
                     the 2023 ACM Web Conf. Austin: ACM, 2023. 154–164. [doi: 10.1145/3543507.3583246]
                 [86]   Zhang HR, Wu QT, Yan JC, Wipf D, Yu PS. From canonical correlation analysis to self-supervised graph neural networks. In: Proc. of
                     the 35th Int’l Conf. on Neural Information Processing Systems. Curran Associates Inc., 2021. 76–89.

                 附中文参考文献
                 [55]   姚暄, 高君宇, 徐常胜. 基于自监督图对比学习的视频问答方法. 软件学报, 2023, 34(5): 2083–2100. http://www.jos.org.cn/1000-
                     9825/6775.htm [doi: 10.13328/j.cnki.jos.006775]

                 作者简介
                 刘子扬, 博士生, 主要研究领域为图机器学习, 推荐系统.
                 王朝坤, 博士, 副教授, 博士生导师, CCF  高级会员, 主要研究领域为数据库理论与系统, 图机器学习.
                 章衡, 博士, 副教授, CCF  专业会员, 主要研究领域为知识计算, 人工智能基础理论, 计算机科学逻辑.
   197   198   199   200   201   202   203   204   205   206   207