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 Networ...
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 Networ...
通过显式地划分开输入节点和输出节点,BiGraphNet使得GNN能够支持一些有效的优化操作,如粗图卷积(coarsened graph convolutions),类似与CNN中跨步卷积的操作;还有输入多个图的卷积操作以及图展开(unpooling),这些都可被用于诸如图自编码器(graph autoencoder),图残差网络(graph residual nets)等模型中。 通过仅在指定的...
在这里,这些转换是由GATE(Graph attention auto-encoders)建模的。为了缓解不同 之间的异质差距,并更好地对齐潜在表示,作者在所提出的SGCMC中建立了一个多视图共享的自动编码器。 【 In order to relieve the heterogeneous gap between different and better align latent representation, we build a multi-view sh...
Graph Relational Topic Model with Higher-order Graph Attention Auto-encodersLearning low-dimensional representations of networked documents is a crucial task for documents linked in network structures. Relational Topic Models (RTMs) have shown their strengths in modeling both document contents and ...
human-activity-recognition hypergraph human-action-recognition skeleton-based-action-recognition graph-attention graph-auto-encoder hypergraph-neural-networks graphneuralnetwork graphconvoltution graphtransformer adaptive-graph hypergraph-transformer Updated May 17, 2024 Python rimo...
deep-learningconvolutional-networksgraph-attentiongraph-networkgenerated-graphsgraph-auto-encoder UpdatedDec 29, 2023 VGraphRNN/VGRNN Star114 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...
图自编码器 (graph autoencoders, GAE) 考虑时间因素的图神经网络 (spatial-temporal graph neural networks, ST-GNN) 讨论图神经网络在各个领域的应用 总结了 GNN 的开源代码、基准数据集和模型评估 潜在的研究方向 介绍 机器学习解决的任务 【严重依赖于工人数据标注】 目标检测 机器翻译 语音识别 目前解决上述任...
graph auto-encoders, contrastive learning.graph auto-encodersGAE (graph auto-encoders), VGAE (variational GAE) (Kipf and Welling, 2016), It uses a simple decoder to reconstruct the adjacency matrix.H=GCN(X,A)~A=f(HHT)H=GCN(X,A)A~=f(HHT)...