To well exploit cluster structure, inspired by these insight analysis, we propose a nodes clustering method, namely, Graph Embedding Clustering: Graph Attention Auto-encoder With Cluster-Specificity Distribution (GEC-CSD). Specifically, to make the decoder part learnable, node attributes reconstruction ...
In this study, we present scGAD, a graph attention autoencoder model with a dual decoder structure for clustering scRNA-seq data. We utilize a graph attention autoencoder with two decoders to learn feature representations of cells in latent space. To ensure that the learned latent feature ...
在本文中,我们将图神经网络划分为五大类别,分别是:图卷积网络(Graph Convolution Networks,GCN)、 图注意力网络(Graph Attention Networks)、图自编码器( Graph Autoencoders)、图生成网络( Graph Generative Networks) 和图时空网络(Graph Spatial-temporal Networks)。 符号定义 1. 图卷积网络(Graph Convolution Netwo...
Recent advances in spatially resolved transcriptomics have enabled comprehensive measurements of gene expression patterns while retaining the spatial context of the tissue microenvironment. Deciphering the spatial context of spots in a tissue needs to us
1. 基于autoencoder的异常检测 上述模型都是针对图的节点异常,并且都基于autoencoder方法。对于一个给定的图G,ae会首先通过encoder把节点原始特征矩阵X通过一些方式(GNN等)转化为隐层特征Z,随后通过decoder用Z对图的邻接矩阵A和特征矩阵X进行重建,得到A^,X^。重建误差,A−A^,X−X^经处理后被当做节点的异常分...
在本文中,我们将图神经网络划分为五大类别,分别是:图卷积网络(Graph Convolution Networks,GCN)、 图注意力网络(Graph Attention Networks)、图自编码器( Graph Autoencoders)、图生成网络( Graph Generative Networks) 和图时空网络(Graph Spatial-temporal Networks)。
2.1 Graph Attentional Autoencoder2.1.1 GAT encoder首先:衡量 nodenode ii 的邻居 NiNi 对于节点 ii 的影响,采用图注意力机制:zl+1i=σ(∑j∈NiαijWzlj)(1)zil+1=σ(∑j∈NiαijWzjl)(1)其中:αijαij is the attention coefficient that indicates the importance of neighbor node jj to node ii...
deep-learningconvolutional-networksgraph-attentiongraph-networkgenerated-graphsgraph-auto-encoder UpdatedDec 29, 2023 VGraphRNN/VGRNN Star116 Code Issues Pull requests Variational Graph Recurrent Neural Networks - PyTorch representation-learningvariational-inferencelink-predictiongraph-convolutional-networksvariational...
为了填补这一空缺,我们提出了一个基于图形自动编码器的多媒体推荐模型(Content-aware Multimedia Recommendation Model with Graph Autoencoder (GraphCAR)),把信息丰富的多媒体内容和用户-项目交互结合起来。具体来说,用户项目交互、用户属性和多媒体内容(图形、视频、音频等),作为自动编码器的输入,为每个用户生成项目偏...
Here, we propose stMVC, a multi-view graph collaborative-learning model that integrates histology, gene expression, spatial location, and biological contexts in analyzing SRT data by attention. Specifically, stMVC adopting semi-supervised graph attention autoencoder separately learns view-specific ...