2. Variational Autoencoders 为什么我们需要Variational Autoencoders? Variational Avtoencoder的最大好处是特能够通过原始数据产生新的数据。而传统的Auto encoder只能够通过原始数据产生相似的数据。 主要思想: 它先学习所有的样本的分布,然后根据这个分布随机产生新的样本。 Encoder 以一个点X作为输入,产生均值 和 。
论文标题:Variational Graph Auto-Encoders 论文作者:Thomas Kipf, M. Welling 论文来源:2016, ArXiv 论文地址:download 论文代码:download 1 Introduce 变分自编码器在图上的应用,该框架可以自行参考变分自编码器。 2 Method 变分图自编码器(VGAE ),整体框架如下: ...
Variational Graph Auto-Encodersarxiv.org/pdf/1611.07308.pdf codespaperswithcode.com/paper/variational-graph-auto-encoders 变分图自编码器(VGAE)是一种基于变分自编码器的图结构数据上的无监督学习框架。VGAE利用潜在变量,学习无向图的可解释i安在表示,如下图: Cora数据集上训练的无监督VGAE模型的潜...
论文地址:Kipf T N, Welling M. Variational graph auto-encoders[J]. NIPS, 2016. 代码地址: https://github.com/tkipf/gae图神经网络可以细分为五类:图卷积网络、图注意力网络、图时空网络、图生成网络和图自…
Semi-implicit graph variational auto-encoder (SIG-VAE) is proposed to expand the flexibility of variational graph auto-encoders (VGAE) to model graph data. SIG-VAE employs a hierarchical variational framework to enable neighboring node sharing for better generative modeling of graph dependency ...
Variational Graph Autoencoders Method Based on Attentional Mechanisms for Overlapping Community Detection It applies a variational graph autoencoder based on attentional mechanisms to learn the representation of nodes in the graph and enhances the representation ... K Wen,M Lin,X Zhu,... - Internati...
6.PyTorch Geometric tutorial: Graph Autoencoders & Variational Graph Autoencoder 0播放 5.Pytorch Geometric tutorial: Aggregation Functions in GNNs 1播放 4.Pytorch Geometric tutorial: Convolutional Layers - Spectral methods 1播放 3.Pytorch Geometric tutorial: Graph attention networks (GAT) implementation ...
《Variational Graph Auto-Encoders (VGAE)》T N. Kipf, M Welling [University of Amsterdam] (2016) http://t.cn/Rf9wcYO
Introduced by Kipf et al. in Variational Graph Auto-Encoders 相关学科:GraphSAGEGraph ReconstructionLink PredictionGraph LearningGraph Clusteringnode2vecHate Speech DetectionGCNGraph EmbeddingGraph Representation Learning 学科讨论 暂无讨论内容,你可以发起讨论 ...
we explore the task of learning to generate graphs that conform to a distribution observed in training data. We propose a variational autoencoder model in which both encoder and decoder are graph-structured. Our decoder assumes a sequential ordering of graph extension steps and we discuss and...