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 "Signed Graph Convolutional Network" (ICDM 2018).

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

Representation learning on large graphs using stochastic graph convolutions.

GraphSAGE and GAT for link prediction.

B站GNN教程资料

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

CFG based program similarity using Graph Neural Networks

[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

Graph Neural Networks

玩转图神经网络和知识图谱的相关算法：GCN,GAT,GAFM,GAAFM,GraphSage,W2V,TRANSe

PyTorch implementation of GraphSAGE.

The task aims at extracting required fields in receipts captured by mobile devices 😄