There are 8 repositories under pytorch-geometric topic.
Geometric GNN Dojo provides unified implementations and experiments to explore the design space of Geometric Graph Neural Networks.
Implementation of Principal Neighbourhood Aggregation for Graph Neural Networks in PyTorch, DGL and PyTorch Geometric
Attention over nodes in Graph Neural Networks using PyTorch (NeurIPS 2019)
A PyTorch implementation of "Signed Graph Convolutional Network" (ICDM 2018).
The official implementation of NeurIPS22 spotlight paper "NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification"
Free hands-on course about Graph Neural Networks using PyTorch Geometric.
[CVPR'22 Best Paper Finalist] Official PyTorch implementation of the method presented in "Learning Multi-View Aggregation In the Wild for Large-Scale 3D Semantic Segmentation"
Implementation of "GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings" in PyTorch
Making self-supervised learning work on molecules by using their 3D geometry to pre-train GNNs. Implemented in DGL and Pytorch Geometric.
gRNAde: Geometric Deep Learning for RNA Design
PyTorch Geometric Signed Directed is a signed/directed graph neural network extension library for PyTorch Geometric. The paper is accepted by LoG 2023.
Topological Graph Neural Networks (ICLR 2022)
Code for SIGGRAPH paper CNNs on Surfaces using Rotation-Equivariant Features
A graph reliability toolbox based on PyTorch and PyTorch Geometric (PyG).
The official implementation for ICLR23 paper "GNNSafe: Energy-based Out-of-Distribution Detection for Graph Neural Networks"
Given an input graph (ArangoDB or PyG) it generates graph embeddings using Low-Code framework built on top of PyG.
Graph Neural Network application in predicting AC Power Flow calculation. Developed with Pytorch Geometric framework. My Master Thesis at Eindhoven University of Technology
A PyTorch Geometric implementation of SimGNN with some extensions.
Gradient gating (ICLR 2023)
B站GNN教程资料
Spatiotemporal datasets collected for network science, deep learning and general machine learning research.
Code of "Analyzing the Expressive Power of Graph Neural Networks in a Spectral Perspective" paper published in ICLR2021
Code for "Graph Neural Networks for Friend Ranking in Large-scale Social Platforms" (WWW 2021).
Various Applications and Descriptions of GNN with Pytorch, Pytorch Geometric.
The implementation code for our paper Wasserstein Embedding for Graph Learning (ICLR 2021).