【论文解读（GMI）《Graph Representation Learning via Graphical Mutual Information Maximization》2】的更多相关文章

论文解读（GMI）《Graph Representation Learning via Graphical Mutual Information Maximization》2

Paper Information 论文作者:Zhen Peng.Wenbing Huang.Minnan Luo.Q. Zheng.Yu Rong.Tingyang Xu.Junzhou Huang论文来源:WWW 2020论文地址:download代码地址:download 前言 1.自监督学习(Self-supervised):属于无监督学习,其核心是自动为数据打标签(伪标签或其他角度的可信标签,包括图像的旋转.分块等等),通过让网络按照既定的规则,对数据打出正确的标签来更好地进行特征表示…

论文解读（GMI）《Graph Representation Learning via Graphical Mutual Information Maximization》

Paper Information 论文作者:Zhen Peng.Wenbing Huang.Minnan Luo.Q. Zheng.Yu Rong.Tingyang Xu.Junzhou Huang论文来源:WWW 2020论文地址:download代码地址:download 前言 1.自监督学习(Self-supervised):属于无监督学习,其核心是自动为数据打标签(伪标签或其他角度的可信标签,包括图像的旋转.分块等等),通过让网络按照既定的规则,对数据打出正确的标签来更好地进行特征表示…

论文阅读 Dynamic Graph Representation Learning Via Self-Attention Networks

4 Dynamic Graph Representation Learning Via Self-Attention Networks link:https://arxiv.org/abs/1812.09430 Abstract 提出了在动态图上使用自注意力 Conclusion 本文提出了使用自注意力的网络结构用于在动态图学习节点表示.具体地说,DySAT使用(1)结构邻居和(2)历史节点表示上的自我注意来计算动态节点表示,虽然实验是在没有节点特征的图上进行的,但DySAT可以很容易地推广到特…

论文解读（SUGRL）《Simple Unsupervised Graph Representation Learning》

Paper Information Title:Simple Unsupervised Graph Representation LearningAuthors: Yujie Mo.Liang Peng.Jie Xu, Xiaoshuang Shi.Xiaofeng ZhuSources:2022 AAAIPaper:downloadCode:download Abstract 作者提出了一种简单的无监督图表示学习方法来进行有效和高效的对比学习.具体而言,通过构造多重损失探索结构信息与邻域信息之…

论文解读（GRCCA）《 Graph Representation Learning via Contrasting Cluster Assignments》

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-…

论文解读GALA《Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learning》

论文信息 Title:<Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learning> Authors:Jiwoong Park.Minsik Lee.H. Chang.Kyuewang Lee.J. Choi Sources:2019 IEEE/CVF International Conference on Computer Vision (ICCV) Paper:Downlo…

论文解读（ N2N）《Node Representation Learning in Graph via Node-to-Neighbourhood Mutual Information Maximization》

论文信息 论文标题:Node Representation Learning in Graph via Node-to-Neighbourhood Mutual Information Maximization论文作者:Wei Dong, Junsheng Wu, Yi Luo, Zongyuan Ge, Peng Wang论文来源:CVPR 2022论文地址:download论文代码:download 1 摘要 在本工作中,我们提出了一种简单而有效的自监督节点表示学习策略,通过直接最大化节点的…

论文解读（MERIT）《Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning》

论文信息 论文标题:Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning论文作者:Ming Jin, Yizhen Zheng, Yuan-Fang Li, Chen Gong, Chuan Zhou, Shirui Pan论文来源:2021, IJCAI论文地址:download 论文代码:download 1 Introduction 创新:融合交叉视图对比和交叉网…

论文解读（SUBG-CON）《Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learning》

论文信息 论文标题:Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learning论文作者:Yizhu Jiao, Yun Xiong, Jiawei Zhang, Yao Zhang, Tianqi Zhang, Yangyong Zhu论文来源:2020 ICDM论文地址:download 论文代码:download 1 Introduction 创新点:提出一种新的子图对比度自监督表示学习方法,利用…

论文解读（GMIM）《Deep Graph Clustering via Mutual Information Maximization and Mixture Model》

论文信息 论文标题:Deep Graph Clustering via Mutual Information Maximization and Mixture Model论文作者:Maedeh Ahmadi, Mehran Safayani, Abdolreza Mirzaei论文来源:2022, arXiv 论文地址:download论文代码:download 1 Introduction 结合高斯混合模型+对比学习. 2 Method 总体框架   2.1 Node Embedding En…