904 skills found · Page 1 of 31
tkipf / GcnImplementation of Graph Convolutional Networks in TensorFlow
tkipf / PygcnGraph Convolutional Networks in PyTorch
lehaifeng / T GCNTemporal Graph Convolutional Network for Urban Traffic Flow Prediction Method
megvii-research / ML GCNPyTorch implementation of Multi-Label Image Recognition with Graph Convolutional Networks, CVPR 2019.
yao8839836 / Text GcnGraph Convolutional Networks for Text Classification. AAAI 2019
mdeff / Cnn GraphConvolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
VeritasYin / STGCN IJCAI 18[IJCAI'18] Spatio-Temporal Graph Convolutional Networks
sungyongs / Graph Based NnGraph Convolutional Networks (GCNs)
Jiakui / Awesome Gcnresources for graph convolutional networks （图卷积神经网络相关资源）
shubhomoydas / Ad ExamplesA collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.
Tiiiger / SGCofficial implementation for the paper "Simplifying Graph Convolutional Networks"
txie-93 / CgcnnCrystal graph convolutional neural networks for predicting material properties.
tkipf / Relational GcnKeras-based implementation of Relational Graph Convolutional Networks
benedekrozemberczki / ClusterGCNA PyTorch implementation of "Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks" (KDD 2019).
tkipf / Keras GcnKeras implementation of Graph Convolutional Networks
guoshnBJTU / ASTGCN 2019 PytorchAttention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting, AAAI 2019, pytorch version
hongsukchoi / Pose2Mesh RELEASEOfficial Pytorch implementation of "Pose2Mesh: Graph Convolutional Network for 3D Human Pose and Mesh Recovery from a 2D Human Pose", ECCV 2020
lshiwjx / 2s AGCNTwo-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition in CVPR19
HazyResearch / HgcnHyperbolic Graph Convolutional Networks in PyTorch.
paulgavrikov / VisualkerasVisualkeras is a Python package to help visualize Keras (either standalone or included in TensorFlow) neural network architectures. It allows easy styling to fit most needs. This module supports layered style architecture generation which is great for CNNs (Convolutional Neural Networks), and a graph style architecture, which works great for most models including plain feed-forward networks.
