在A Comprehensive Survey on Graph Neural Networkshttps://arxiv.org/pdf/1901.00596.pdf中提出了将图神经网络进一步地分为Recurrent graph neural networks (RecGNNs)递归图神经网络、Convolutional graph neural networks (ConvGNNs)卷积图神经网络、Graph autoencoders (GAEs)图自动编码器和 Spatial-temporal graph ne...
目前基于GCN的自编码器的方法主要有:Graph Autoencoder (GAE)和Adversarially Regularized Graph Autoencoder (ARGA) 图自编码器的其它变体有: Network Representations with Adversarially Regularized Autoencoders (NetRA) Deep Neural Networks for Graph Representations (DNGR) Structural Deep Network Embedding (SDNE) ...
我们将首先回顾基于GCN的AutoEncoder,然后总结这一类别中的其他变体。 目前基于GCN的自编码器的方法主要有:Graph Autoencoder (GAE)和Adversarially Regularized Graph Autoencoder (ARGA) 图自编码器的其它变体有: Network Representations with Adversarially Regularized Autoencoders (NetRA) Deep Neural Networks for Gr...
To address the aforementioned issues, we propose the denoising autoencoder integrated with self-supervised learning (SSL) in graph neural networks (DAS-GNN). In DAS-GNN, the query extraction module based on denoising autoencoder can mine multiple user interests and assist long-term interest to ...
graph neural networks 图神经网络 graph convolutional networks 图卷积神经网络 graph representation learning 图表示学习 graph autoencoder 图自动编码器 network embedding 网络嵌入 pattern recognition 模式识别 data mining 数据挖掘 object detection 目标检测 ...
TrustAGI-Lab/Awesome-Graph-Neural-Networks Star2.2k Code Issues Pull requests Paper Lists for Graph Neural Networks deep-learningconvolutional-networksgraph-attentiongraph-networkgenerated-graphsgraph-auto-encoder UpdatedDec 29, 2023 VGraphRNN/VGRNN ...
3.3 Graph Autoencoders 3.4 Spatial-Temporal Graph Neural Networks 4 GRAPH NEURAL NETWORKS PIPELINE 4.1 Graph Definition 4.2 Task Definition 4.3 Model Definition 5 PIPELINE APPLICATION TO EDA 5.1 Logic Synthesis 5.2 Verification and Signoff 5.3 Floorplanning ...
Examples include variational autoencoders (VAEs)134, generative adversarial networks (GANs)135, reinforcement learning136, recurrent neural networks (RNNs)137, and flow-based generative models138,139,140. Several architectures of VAEs have been developed to work with different types of input data, ...
For example, recurrent [5], [6], convolutional [4], [7], [8], [9] and spatial–temporal [10] graph neural networks as well as graph autoencoders [11], [12] and graph transformer models [13], [14] have been reported in literature. A graph G=(V,E) is defined as a set of...
Here we present MolCLR (Molecular Contrastive Learning of Representations via Graph Neural Networks), a self-supervised learning framework that leverages large unlabelled data (~10 million unique molecules). In MolCLR pre-training, we build molecule graphs and develop graph-neural-network encoders to...