在A Comprehensive Survey on Graph Neural Networks arxiv.org/pdf/1901.0059 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 neural ...
Methods We propose an adversarial regularized autoencoder graph neural network algorithm, named Stacked Adversarial Regularization for Microbe-Disease Associations Prediction (SARMDA), for predicting associations between microbes and diseases. First, we integrate topological structural similarity a...
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) ...
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) ...
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, ...
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 ...
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...
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 ...
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 ...
mainly made use of traditional machine learning methods to predict lncRNA subcellular localization and spent a lot of resources on feature extraction. For instance, Zhen C, et al. [25] specially used an unsupervised stacked autoencoder model to obtain high-level features from k-mer low-level ...