论文解读（GGD）《Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination》

论文信息论文标题:Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination论文作者:Yizhen Zheng, Shirui Pan, Vincent Cs Lee, Yu Zheng, Philip S. Yu论文来源:2022,NeurIPS论文地址:download 论文代码:download 1 Introduction…

论文解读（MLGCL）《Multi-Level Graph Contrastive Learning》

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.…

论文解读（GRACE）《Deep Graph Contrastive Representation Learning》

Paper Information 论文标题:Deep Graph Contrastive Representation Learning论文作者:Yanqiao Zhu, Yichen Xu, Feng Yu, Q. Liu, Shu Wu, Liang Wang论文来源:2020, ArXiv论文地址:download 代码地址:download Abstract 在本文中,作者提出了一个利用节点级对比目标的无监督图表示学习框架.具体来说,通过破坏原始图去生成两个视图,并通过最大化这两个视图…

论文解读（GCC）《GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training》

论文信息论文标题:GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training论文作者:Jiezhong Qiu, Qibin Chen, Yuxiao Dong, Jing Zhang, Hongxia Yang, Ming Ding, Kuansan Wang, Jie Tang论文来源:2020, KDD论文地址:download论文代码:download 1 Introduction 本文的预训练任务:子图实例判…

论文解读（GCA）《Graph Contrastive Learning with Adaptive Augmentation》

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:download论文代码:download 1 介绍出发角度:倾向于保持重要的结构和属性不变,同时干扰可能不重要的边连接和特征. 自适应数据增强方面: 拓扑结构:基于节点中心性度量,突出重要连接: 语义信息…

论文解读（GROC）《Towards Robust Graph Contrastive Learning》

论文信息论文标题:Towards Robust Graph Contrastive Learning论文作者:Nikola Jovanović, Zhao Meng, Lukas Faber, Roger Wattenhofer论文来源:2021, arXiv论文地址:download 论文代码:download 1 Introduction 创新点:从对抗攻击和对抗防御考虑数据增强策略. 2 Graph robust contrastive learning 2.1 Background 目…

论文解读（SimGRACE）《SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation》

论文信息论文标题:SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation论文作者:Jun Xia, Lirong Wu, Jintao Chen, Bozhen Hu, Stan Z. Li论文来源:2022, WWW论文地址:download 论文代码:download 1 Introduction 对比学习种数据增强存在的三个问题: First, the augmentati…

论文解读（AGC）《Attributed Graph Clustering via Adaptive Graph Convolution》

论文信息论文标题:Attributed Graph Clustering via Adaptive Graph Convolution论文作者:Xiaotong Zhang, Han Liu, Qimai Li, Xiao-Ming Wu论文来源:2019, IJCAI论文地址:download 论文代码:download 1 Introduction 关于GNN 是低通滤波器的好文. 2 Method 2.1 Graph Convolution 2.1.1 Basic idea 为正式定义图…

论文解读（SelfGNN）《Self-supervised Graph Neural Networks without explicit negative sampling》

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:download 论文代码:download 1 介绍本文核心贡献: 使用孪生网络隐式实现对比学习: 本文提出四种特征增强方式(FA): 2 相关工作 Graph Neural Networks GCN…

论文解读《Momentum Contrast for Unsupervised Visual Representation Learning》俗称 MoCo

论文题目:<Momentum Contrast for Unsupervised Visual Representation Learning> 论文作者: Kaiming He.Haoqi Fan. Yuxin Wu. Saining Xie. Ross Girshick 论文来源:arXiv 论文来源:https://github.com/facebookresearch/moco 1 主要思想文章核心思想是使用基于 Contrastive learning 的方式自监督的训练一个图片表…

论文解读（DGI）《DEEP GRAPH INFOMAX》

论文标题:DEEP GRAPH INFOMAX 论文方向:图像领域论文来源:2019 ICLR 论文链接:https://arxiv.org/abs/1809.10341 论文代码:https://github.com/PetarV-/DGI 摘要 DGI,一种以无监督的方式学习图结构数据中节点表示的一般方法.DGI 依赖于最大限度地扩大图增强表示和目前提取到的图信息之间的互信息--两者都是使用已建立的图卷积网络体系结构导出的.对于图增强表示,是根据目标节点所生成的子图,因此可以用于下游节点的…

论文解读（DCRN）《Deep Graph Clustering via Dual Correlation Reduction》

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论文来源:2022, AAAI论文地址:download 论文代码:download 1 介绍表示崩塌问题:倾向于将所有数据映射到相同表示. 2 方法 2.1 整体框架该框架包括两个模块…

论文解读（IGSD）《Iterative Graph Self-Distillation》

论文信息论文标题:Iterative Graph Self-Distillation论文作者:Hanlin Zhang, Shuai Lin, Weiyang Liu, Pan Zhou, Jian Tang, Xiaodan Liang, Eric P. Xing论文来源:2021, ICLR论文地址:download 论文代码:download 1 Introduction 创新点:图级对比. 2 Method 整体框架如下: 2.1 Iterative Graph Self-Distil…

论文解读（SAGPool）《Self-Attention Graph Pooling》

论文信息论文标题:Self-Attention Graph Pooling论文作者:Junhyun Lee, Inyeop Lee, Jaewoo Kang论文来源:2019, ICML论文地址:download 论文代码:download 1 Introduction 图池化三种类型: Topology based pooling: Hierarchical pooling:(使用所有从 GNN 获得的节点表示) Hierarchical pooling: 关于 Hierarchical p…

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…

论文解读（FedGAT）《Federated Graph Attention Network for Rumor Detection》

论文信息论文标题:Federated Graph Attention Network for Rumor Detection论文作者:Huidong Wang, Chuanzheng Bai, Jinli Yao论文来源:2022, arXiv论文地址:download 论文代码:download 1 Introduction 现有的谣言检测模型都是为单一的社交平台构建的,这忽略了跨平台谣言的价值.本文将联邦学习范式与双向图注意网络谣言检测模型相结合,提出了用于谣言检测的联邦图注意网络(Fed…

论文解读（AGCN）《 Attention-driven Graph Clustering Network》

Paper Information Title:<Attention-driven Graph Clustering Network>Authors:Zhihao Peng, Hui Liu, Yuheng Jia, Junhui HouSource:2021, ACM MultimediaOther:1 Citations, 46 ReferencesPaper:DownloadCode:DownloadTask: Deep Clustering.Graph Clustering.Graph…

论文解读（VGAE）《Variational Graph Auto-Encoders》

Paper Information Title:Variational Graph Auto-EncodersAuthors:Thomas Kipf, M. WellingSoures:2016, ArXivOthers:1214 Citations, 14 References 1 A latent variable model for graph-structured data VGAE 使用了一个 GCN encoder 和一个简单的内积 decoder ,架构如下图所示: Defini…

论文解读（node2vec）《node2vec Scalable Feature Learning for Networks》

论文题目:<node2vec Scalable Feature Learning for Network>发表时间: KDD 2016 论文作者: Aditya Grover;Aditya Grover; Jure Leskovec论文地址: DownloadGithub: Go 概述 node2vec is an algorithmic framework for representational learning on graphs. Given any graph, it…

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…