Stars
A Survey of Learning from Graphs with Heterophily
A pytorch implementation of H2GCN raised in the paper "Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs".
This repository contains the resources on graph neural network (GNN) considering heterophily.
A dgl implementation of Jumping Knowledge Networks (arXiv 1806.03536)
Python package built to ease deep learning on graph, on top of existing DL frameworks.
Pytorch implementation of the Attention-based Graph Neural Network(AGNN)
The code of paper "Block Modeling-Guided Graph Convolutional Neural Networks".
PyTorch_Geometric实现的JK-Nets（Jumping Knowledge Network），其中也包括了baseline的GCN和GAT。数据集使用的「Cora、Citeseer、Pubmed」
This is an improvement of baesline on the ogbn-arxiv dataset.
Graph Neural Network Library for PyTorch
Paper Lists for Graph Neural Networks
Measuring and Improving the Use of Graph Information in Graph Neural Networks
My implementation of the original GAT paper (Veličković et al.). I've additionally included the playground.py file for visualizing the Cora dataset, GAT embeddings, an attention mechanism, and entr…
Code for "Graph Neural Network on Electronic Health Records for Predicting Alzheimer’s Disease"
Must-read papers on graph neural networks (GNN)
PyTorch implementation of the U-Net for image semantic segmentation with high quality images
FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.
A simplified implemention of Faster R-CNN that replicate performance from origin paper
This is the PyTorch implementation of VGG network trained on CIFAR10 dataset