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摘要: 论文信息 论文标题:Graph Contrastive Learning with Adaptive Augmentation论文作者:Yanqiao Zhu、Yichen Xu3、Feng Yu4、Qiang Liu、Shu Wu、Liang Wang论文来源:2021, WWW论文地址:down 阅读全文
posted @ 2022-04-12 19:47 Learner- 阅读(160) 评论(0) 推荐(0) 编辑
摘要: Example: class load_data(Dataset): def __init__(self, dataset): self.x = np.loadtxt('data/{}.txt'.format(dataset), dtype=float) self.y = np.loadtxt('d 阅读全文
posted @ 2022-04-11 16:07 Learner- 阅读(7) 评论(0) 推荐(0) 编辑
摘要: 论文信息 论文标题:Multi-view Contrastive Graph Clustering论文作者:Erlin Pan、Zhao Kang论文来源:2021, NeurIPS论文地址:download论文代码:download 1 介绍 本文贡献: 使用Graph Filter 过滤了高阶噪 阅读全文
posted @ 2022-04-11 11:10 Learner- 阅读(174) 评论(1) 推荐(0) 编辑
摘要: https://zhuanlan.zhihu.com/p/138153530 矩阵与数值计算 阅读全文
posted @ 2022-04-10 21:56 Learner- 阅读(29) 评论(0) 推荐(0) 编辑
摘要: 论文信息 论文标题:Self-supervised Learning on Graphs: Contrastive, Generative,or Predictive论文作者:Lirong Wu, Haitao Lin, Cheng Tan,Zhangyang Gao, and Stan.Z.Li论 阅读全文
posted @ 2022-04-10 19:19 Learner- 阅读(74) 评论(0) 推荐(0) 编辑
摘要: 论文信息 论文标题:Multi-Level Graph Contrastive Learning论文作者:Pengpeng Shao, Tong Liu, Dawei Zhang, J. Tao, Feihu Che, Guohua Yang论文来源:2021, Neurocomputing论文地址 阅读全文
posted @ 2022-04-10 16:58 Learner- 阅读(421) 评论(0) 推荐(0) 编辑
摘要: 论文信息 论文标题:Structural and Semantic Contrastive Learning for Self-supervised Node Representation Learning论文作者: Kaize Ding 、Yancheng Wang 、Yingzhen Yang、 阅读全文
posted @ 2022-04-09 14:39 Learner- 阅读(138) 评论(0) 推荐(0) 编辑
摘要: 论文信息 论文标题:Graph Representation Learning via Contrasting Cluster Assignments论文作者:Chun-Yang Zhang, Hong-Yu Yao, C. L. Philip Chen, Fellow, IEEE and Yue- 阅读全文
posted @ 2022-04-06 22:24 Learner- 阅读(109) 评论(0) 推荐(0) 编辑
摘要: 论文信息 论文标题:Self-supervised Learning on Graphs: Contrastive, Generative,or Predictive论文作者:Lirong Wu, Haitao Lin, Cheng Tan,Zhangyang Gao, and Stan.Z.Li论 阅读全文
posted @ 2022-04-06 16:50 Learner- 阅读(67) 评论(0) 推荐(0) 编辑
摘要: 论文信息 论文标题:Contrastive Self-supervised Learning for Graph Classification论文作者:Jiaqi Zeng, Pengtao Xie论文来源:2020, AAAI论文地址:download 论文代码:download 1 Introd 阅读全文
posted @ 2022-04-06 11:09 Learner- 阅读(67) 评论(0) 推荐(0) 编辑
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