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""
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
Representation learning on large graphs using stochastic graph convolutions.
GraphSAGE and GAT for link prediction.
Gradient gating (ICLR 2023)
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)
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.
CFG based program similarity using Graph Neural Networks
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