An autoML framework & toolkit for machine learning on graphs.

Protein Graph Library

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)

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

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.

Implementation of MolCLR: "Molecular Contrastive Learning of Representations via Graph Neural Networks" in PyG.

[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"

GOOD: A Graph Out-of-Distribution Benchmark [NeurIPS 2022 Datasets and Benchmarks]

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"