A distributed graph deep learning framework.

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 "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 "Graph Classification Using Structural Attention" (KDD 2018).

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

Representation learning on large graphs using stochastic graph convolutions.

B站GNN教程资料

GraphSAGE and GAT for link prediction.

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

Graph Neural Networks

Gradient gating (ICLR 2023)

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。

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

Senior Capstone Project: Graph-Based Product Recommendation

CFG based program similarity using Graph Neural Networks

An example project for training a GraphSAGE Model, and setup a Real-time Fraud Detection Web Service(Frontend and Backend) with NebulaGraph Database and DGL.