graphsage

A distributed graph deep learning framework.

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

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

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.

Graph Neural Networks

GraphSAGE and GAT for link prediction.

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

Senior Capstone Project: Graph-Based Product Recommendation

Gradient gating (G^2)

CFG based program similarity using Graph Neural Networks

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

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

PyTorch implementation of GraphSAGE.

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.