pytorch-geometric

#计算机科学#Protein Graph Library

#计算机科学#An autoML framework & toolkit for machine learning on graphs.

Geometric GNN Dojo provides unified implementations and experiments to explore the design space of Geometric Graph Neural Networks (ICML 2023)

#计算机科学#Free hands-on course about Graph Neural Networks using PyTorch Geometric.

Implementation of Principal Neighbourhood Aggregation for Graph Neural Networks in PyTorch, DGL and PyTorch Geometric

The official implementation of NeurIPS22 spotlight paper "NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification"

The official implementation for ICLR23 spotlight paper "DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion"

#计算机科学#Implementation of MolCLR: "Molecular Contrastive Learning of Representations via Graph Neural Networks" in PyG.

Attention over nodes in Graph Neural Networks using PyTorch (NeurIPS 2019)

#计算机科学#A PyTorch implementation of "Signed Graph Convolutional Network" (ICDM 2018).

#计算机科学#[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"

gRNAde: Geometric Deep Learning for 3D RNA inverse design (ICLR 2025 Spotlight)

#计算机科学#GOOD: A Graph Out-of-Distribution Benchmark [NeurIPS 2022 Datasets and Benchmarks]

Making self-supervised learning work on molecules by using their 3D geometry to pre-train GNNs. Implemented in DGL and Pytorch Geometric.

B站GNN教程资料

Implementation of "GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings" in PyTorch

#计算机科学#PyTorch Geometric Signed Directed is a signed/directed graph neural network extension library for PyTorch Geometric. The paper is accepted by LoG 2023.

#计算机科学#Topological Graph Neural Networks (ICLR 2022)

#计算机科学#Graph Neural Network application in predicting AC Power Flow calculation. Developed with Pytorch Geometric framework. My Master Thesis at Eindhoven University of Technology

Code for SIGGRAPH paper CNNs on Surfaces using Rotation-Equivariant Features