下图所示便是作者提出的layer -wise linear propagation rule 图中的D(-1/2)A D(-1/2)为拉普拉斯矩阵, 至于为什么使用拉普拉斯矩阵有一些数学证明,感兴趣的可以查看这篇文章如何理解 Graph Convolutional Network(GCN)?,本质上是对邻接矩阵A作对称归一化, 然后我们通过一个节点特征对 节点的一阶邻边做特征聚合最...
今天给大家介绍的是图卷积神经网络GCN的开山之作《Semi-Supervised Classification With Graph Convolutional Networks》,将按照论文中叙事的顺序进行解析。 摘要 提出了一种适用于图结构数据的半监督学习方法,这种图神经网络是传统的卷积神经网络的一种变体。 简介 本文研究的问题是给一个图中的节点(node)进行分类,我们...
3. 《SEMI-SUPERVISED CLASSIFICATION WITH GRAPH CONVOLUTIONAL NETWORKS》论文阅读(一)(4071) 4. error C2678: 二进制“<”: 没有找到接受“const _Ty”类型的左操作数的运算符(2832) 5. python 内存地址赋值(2499) 评论排行榜 1. 《Reweighted Random Walks for Graph Matching》论文阅读(10) 2. ...
Paper Information Titlel:《Semi-Supervised Classification with Graph Convolutional Networks》Authors:Thomas Kipf, M. WellingSource:2016, ICLRPaper:Downl
Graph convolutional networks (GCNs), which rely on graph structures to aggregate information of neighbors to output robust node embeddings, have been becoming a popular model for semi-supervised classification tasks. However, most existing GCNs ignore the importance of the quality of graph structures,...
论文笔记:Semi-Supervised Classification with Graph Convolutional Networks,程序员大本营,技术文章内容聚合第一站。
1、论文笔记1:SEMI-SUPERVISED CLASSIFICATION WITH GRAPH CONVOLUTIONAL NETWORKS,程序员大本营,技术文章内容聚合第一站。
Graph Laplacian Regularized Graph ConvolutionalNetworks for Semi-supervised LearningBo Jiang, Doudou LinSchool of Computer Science and TechnologyAnhui UniversityHefei, Chinajiangbo@ahu.edu.cnAbstractRecently, graph convolutional network (GCN) has been widely used for semi-supervised classif i cation and de...
上面这部分大概就是用切比雪夫多项式来计算了图上的谱卷积(spetral graph convolution),这里就涉及到了图的傅里叶变换。 先回忆一下一般时序信号上的傅里叶变换是怎样的。 对于一个连续周期时间信号 ,假设它的周期是 ,可以将它展开为傅里叶级数: 利用欧拉公式: ...
Graph convolutional networks (GCNs), as an extension of classic convolutional neural networks (CNNs) in graph processing, have achieved good results in completing semi-supervised learning tasks. Traditional GCNs usually use fixed graph to complete various semi-supervised classification tasks, such as ch...