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摘要: 论文信息 论文标题:Deep Graph Clustering via Dual Correlation Reduction论文作者:Yue Liu, Wenxuan Tu, Sihang Zhou, Xinwang Liu, Linxuan Song, Xihong Yang, En Zhu论文来 阅读全文
posted @ 2022-04-20 22:18 Learner- 阅读(84) 评论(0) 推荐(0) 编辑
摘要: 论文信息 论文标题:Graph Contrastive Clustering论文作者:Huasong Zhong, Jianlong Wu, Chong Chen, Jianqiang Huang, Minghua Deng, Liqiang Nie, Zhouchen Lin, Xian-Shen 阅读全文
posted @ 2022-04-18 23:13 Learner- 阅读(74) 评论(0) 推荐(0) 编辑
摘要: 论文信息 论文标题:Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks论文作者:Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang L 阅读全文
posted @ 2022-04-18 11:01 Learner- 阅读(20) 评论(0) 推荐(0) 编辑
摘要: 论文信息 论文标题:Self-supervised Graph Neural Networks without explicit negative sampling论文作者:Zekarias T. Kefato, Sarunas Girdzijauskas论文来源:2021, WWW论文地址:dow 阅读全文
posted @ 2022-04-14 22:59 Learner- 阅读(131) 评论(0) 推荐(2) 编辑
摘要: 论文信息 论文标题:Large-Scale Representation Learning on Graphs via Bootstrapping论文作者:Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, Rémi Munos, 阅读全文
posted @ 2022-04-14 16:50 Learner- 阅读(96) 评论(0) 推荐(0) 编辑
摘要: https://zhuanlan.zhihu.com/p/36522776 阅读全文
posted @ 2022-04-14 11:20 Learner- 阅读(11) 评论(0) 推荐(0) 编辑
摘要: 论文信息 论文标题:Automated Self-Supervised Learning for Graphs论文作者:Wei Jin, Xiaorui Liu, Xiangyu Zhao, Yao Ma, Neil Shah, Jiliang Tang论文来源:2022, ICLR论文地址:dow 阅读全文
posted @ 2022-04-13 17:07 Learner- 阅读(82) 评论(0) 推荐(0) 编辑
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(68) 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(3) 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(66) 评论(0) 推荐(0) 编辑
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