Scalable GNNs:PyG supports the implementation of Graph Neural Networks that can scale to large-scale graphs. Such application is challenging since the entire graph, its associated features and the GNN parameters cannot fit into GPU memory. Many state-of-the-art scalability approaches tackle this cha...
A PyTorch implementation of "Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information" (WSDM 2021) - EnyanDai/FairGNN
A Convolutional Gated Recurrent Neural Network for Epileptic Seizure PredictionABSTRACT在本文中,我们提出了一种卷积门控递归神经网络(CGRNN)来预测癫痫发作,基于从EEG数据中提取的代表信号的时间方面和频率方面的特征。使用波士顿儿童医院收集的数据集,CGRNN可以提前35分钟至5分钟预测癫痫发作。我们的实验结 ...
CapsGNN A PyTorch implementation of Capsule Graph Neural Network (ICLR 2019). Abstract The high-quality node embeddings learned from the Graph Neural Networks (GNNs) have been applied to a wide range of node-based applications and some of them have achieved state-of-the-art (SOTA) performance...
1000- ggnn.pytorch: A PyTorch Implementation of Gated Graph Sequence Neural Networks (GGNN). 1000- visual-interaction-networks-pytorch: This's an implementation of deepmind Visual Interaction Networks paper using pytorch 1000- adversarial-patch: PyTorch实现对抗补丁。 1000- Prototypical-Networks-for-Few...
MMDetection: MMDetection is an open source object detection toolbox, a part of the open-mmlab project developed byMultimedia Laboratory, CUHK. neural-dream: A PyTorch implementation of the DeepDream algorithm. Creates dream-like hallucinogenic visuals. ...
PubMed数据集是一个广泛用于图神经网络(GNN)研究的基准数据集,主要用于节点分类任务。其由生物医学文献组成,每篇文献被视为一个节点,引用关系被视为边。该数据集包含三类糖尿病相关的论文,每个节点都带有特征向量和标签。 数据集一共有19717个节点,每一个节点代表一篇生物医学文献,每个节点有一个500维的特征向量,用...
GCN是GNN的一个分支,全称为图卷积神经网络,顾名思义,GCN是在图上进行"卷积"操作的GNN,这里用引号是因为,GCN的操作并不是卷积神经网络里的那个卷积,这里的卷积,是因为GCN的运算是在聚合节点周围其他节点的信息,与卷积神经网络(CNN)的行为类似。不过话说回来,CNN里的"卷积",也并不是不是数学意义上的卷积。
DeeperGNN Fix a typo Aug 30, 2020 LICENSE Create LICENSE Jun 2, 2020 README.md Update README.md Oct 11, 2022 README GPL-3.0 license This repository is an official PyTorch implementation of DAGNN in "Towards Deeper Graph Neural Networks" (KDD2020). Our implementation is mainly based onPyTo...
Scalable GNNs: PyG supports the implementation of Graph Neural Networks that can scale to large-scale graphs. Such application is challenging since the entire graph, its associated features and the GNN parameters cannot fit into GPU memory. Many state-of-the-art scalability approaches tackle this ...