Representation learning on large graphs using stochastic graph convolutions.

A distributed graph deep learning framework.

PyTorch Implementation and Explanation of Graph Representation Learning papers: DeepWalk, GCN, GraphSAGE, ChebNet & GAT.

Simple reference implementation of GraphSAGE.

Implementation and experiments of graph neural netwokrs, like gcn,graphsage,gat,etc.

A PyTorch implementation of "Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks" (KDD 2019).

A PyTorch implementation of GraphSAGE. This package contains a PyTorch implementation of GraphSAGE.

A PyTorch implementation of "Graph Wavelet Neural Network" (ICLR 2019)

The sample codes for our ICLR18 paper "FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling""

[ICLR 2020; IPDPS 2019] Fast and accurate minibatch training for deep GNNs and large graphs (GraphSAINT: Graph Sampling Based Inductive Learning Method).

1. Use BERT, ALBERT and GPT2 as tensorflow2.0's layer. 2. Implement GCN, GAN, GIN and GraphSAGE based on message passing.

A PyTorch implementation of "Signed Graph Convolutional Network" (ICDM 2018).

A PyTorch implementation of "Graph Classification Using Structural Attention" (KDD 2018).

B站GNN教程资料

Some GNNs are implemented using PyG for node classification tasks, including: GCN, GraphSAGE, SGC, GAT, R-GCN and HAN (Heterogeneous Graph Attention Network), which will continue to be updated in the future.

Representation learning on large graphs using stochastic graph convolutions.

A PyTorch implementation of of E-GraphSAGE.

GNN方法和模型的Pytorch实现。Pytorch implementation of GNN.

GraphSAGE and GAT for link prediction.

用pytorch 方法复现了二十多个经典的推荐算法论文，其中包含排序论文和推荐召回论文，并在demo里面选了一个召回模型和排序模型的运行示例。

The pytorch implementation of E-GraphSAGE and E-ResGAT, two solutions for intrusion detection.

Graph convolutional networks (GCN), graphSAGE and graph attention networks (GAT) for text classification

Some GNNs are implemented using PyG for link prediction tasks, including: GCN, GraphSAGE, GAT, Node2Vec、RGCN、HGT and HAN, which will continue to be updated in the future.

Graph Neural Networks

An PyTorch implementation of graph neural networks (GCN, GraphSAGE and GAT) that can be simply imported and used.

The code for GCN, GAT and Graphsage based on pytorch.

Gradient gating (ICLR 2023)

PyG (a geometric extension library for PyTorch) implementation of several Graph Neural Networks (GNNs): GCN, GAT, GraphSAGE, etc.

Senior Capstone Project: Graph-Based Product Recommendation

[ASAP 2020; FPGA 2020] Hardware architecture to accelerate GNNs (common IP modules for minibatch training and full batch inference)