1.2 Structure-wise Augmentation 分为四级: edge addition/dropping,node addition/dropping,graph diffusion,graph sampling。 1.2.1 Edge Addition/Dropping 基于图稀疏性的图结构优化方法[6][7],基于图结构整洁性的方法[3],Edge Addition/Dropping不仅可以是任务无关的,而且还可以通过将拓扑(例如,作为可学习分布)形...
59 A survey on weak optimal transport 1:01:44 Conformal Walk Dimension_ Its Universal Value and the Non-attainment for the Sie 30:51 EKR-Module Property 49:07 How Round is a Jordan Curve_ 24:47 Isomorphic reverse isoperimetry and Lipschitz extension 1:04:17 Knotted Objects Confined to ...
graph diffusion graph sampling 1.2.1 Edge Addition/Dropping 即 保留原始节点顺序,对邻接矩阵种的元进行改写。 基于图稀疏性(graph sparsification)的图结构优化方法 [8、9],基于图结构整洁性(graph sanitation)的方法 [3],以及图采样(graph sampling)。
A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions 3. 快速浏览通道:思维导图 如果看不清楚的话可以去评论区下载源文件~ 1. Intro 1.1 推荐算法发展历史 1.1.1 Shallow Models 协同过滤CF,代表方法矩阵分解MF 《Matrix factorization techniques for recommender systems...
59 A survey on weak optimal transport 1:01:44 Conformal Walk Dimension_ Its Universal Value and the Non-attainment for the Sie 30:51 EKR-Module Property 49:07 How Round is a Jordan Curve_ 24:47 Isomorphic reverse isoperimetry and Lipschitz extension 1:04:17 Knotted Objects Confined to ...
Diffusion Graph Convolution与Spectral Graph Convolution相似性证明 其实维基本科对Laplacian matrix的定义上写得很清楚,国内的一些介绍中只有第一种定义。这让我在最初看文献的过程中感到一些的困惑,特意写下来,帮助大家避免再遇到类似的问题。 为什么GCN要用拉普拉斯矩阵? 拉普拉斯矩阵矩阵有很多良好的性质,这里写三点我...
论文标题:A Survey on Graph Structure Learning: Progress and Opportunities论文作者:Yanqiao Zhu, Weizhi Xu, Jinghao Zhang, Yuanqi Du, Jieyu Zhang, Qiang Liu, Carl Yang, Shu Wu论文来源:2022,arXiv论文地址:download 论文代码:download 1 Introduction图结构学习的出发点:GNN 的成功可以归因于它们能够同时...
Graph-based anomaly detection and description: a survey Adversarial Attack and Defense on Graph Data: A Survey Suspicious behavior detection: Current trends and future directions False information on web and social media: A survey Misinformation in Social Media: Definition, Manipulation, and Detection ...
扩散模型(Diffusion Model)是一种新兴的生成式模型,其首先向数据分布中逐步添加随机噪声到预设的先验分布,然后通过学习其逆过程来重建新的数据样本。自2019年第一个扩散模型范式被提出以来,其强大的生成能力引发了研究热潮。通常而言,扩散模型具有三种生成范式Score Matching with Langevin Dynamics (SMLD), Denoising Dif...
IJCAI‘23 Survey Track: Papers on Graph Pooling (GNN-Pooling) - LiuChuang0059/graph-pooling-papers