#计算机科学#A distributed graph deep learning framework.
Implementation and experiments of graph neural netwokrs, like gcn,graphsage,gat,etc.
#计算机科学#A PyTorch implementation of "Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks" (KDD 2019).
#计算机科学#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).
#自然语言处理#1. Use BERT, ALBERT and GPT2 as tensorflow2.0's layer. 2. Implement GCN, GAN, GIN and GraphSAGE based on message passing.
#计算机科学#A PyTorch implementation of "Signed Graph Convolutional Network" (ICDM 2018).
#计算机科学#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.
#计算机科学#GraphSAGE and GAT for link prediction.
Gradient gating (ICLR 2023)
#计算机科学#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