Simplifying Graph Convolutional Networks SGC代码(pytorch)一、背景介绍GCN的灵感来源于深度学习方法,因此可能继承了不必要的复杂度以及冗余计算。本文作者通过去除GCN层间的非线性、将结果函数变为简单的线性…
3.2节从谱图卷积的理论分析了GCN的有效性,GCN对应于图谱上的固定过滤器,在图中添加自循环,即重正则化技巧,有效的缩小了图的频谱。实验表明,SGC是低通滤波器,在图上产生平滑的特征。因此,在附近的节点共享类似的表示和预测。 拉普拉斯矩阵分解后的特征值就是频率,从图中可以看出SGC本质上是个低频过滤器。图中K为...
每个图中的卷积层和节点表示都是使用三个策略来更新特征传播线性转换 逐点非线性激活Featurepropagation特征传播GCN的特征传播是区别MLP的,因为每一层的...图卷积方法(SimplifyingGraphConvolutional)。通过消除GCN层之间的非线性计算,通过将得到的函数折叠成一个线性变换来减少GCN带来的额外复杂度,并且从图卷积的根源 ...
This repo contains an example implementation of the Simple Graph Convolution (SGC) model, described in the paper Simplifying Graph Convolutional Networks. SGC removes the nonlinearities and collapes the weight matrices in Graph Convolutional Networks (GCNs) and is essentially a linear model. For an ...
Simplifying and attending graph convolutionSimple graph convolution (SGC) achieves competitive classification accuracy to graph convolutional networks (GCNs) in various tasks while being computationally more efficient and fitting fewer parameters. However, the width of SGC is narrow due to the over-...
Simplifying Graph Convolutional Networks Updates As pointed out by #23, there was a subtle bug in our preprocessing code for the reddit dataset. After fixing this bug, SGC achieves a F1 score of 95.0 (previously, it was 94.9). Practical advice: it is often very helpful to normalize the fea...
Complex & Intelligent Systems - Simple graph convolution (SGC) achieves competitive classification accuracy to graph convolutional networks (GCNs) in various tasks while being computationally...Liu, XunLei, FangyuanXia, GuoqingZhang, YikuanWei, Wenguo...
SGC是个好东西,对于理解GCN来说帮助很大。 大概关系是这样的: 1 人类直观思路:节点A的类别和节点A的邻节点有关,直接聚合,简单的操作,节点A的一阶邻节点的node features和节点A的node features直接做一个sum的操作,如果edge有weights则标准化之后weight sum(为了方便概括有权和有向,统一用weight sum来描述); 2 ...
Simplifying Graph Convolutional Networks Veritas 学生 5 人赞同了该文章 摘要 GCN和它的许多变形(GCNs)的灵感都来自于其它深度学习领域的经验,很多时候把模型做得太过复杂。本文提出了一个非常简单有效的模型(SGC),在精度和计算效率上都超过了很多复杂模型。文章还说明了模型与图滤波器的联系,解释了Re-normalization...
技术标签: SGC gcn Graph Convolutional Network文章目录 1 相关介绍 1.1 Simple Graph Convolution (SGC)提出的背景 1.2 SGC效果 2 Simple Graph Convolution 简化的图卷积 2.1 符号定义 2.2 图卷积网络GCN GCN vs MLP Feature propagation 特征传播 Feature transformation and nonlinear transitio... 查看原文 图深度...