PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data. It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning , from a variety of published papers.

PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data. It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers.

PyG is a library built upon PyTorch to easily write and train Graph Neural Networks for a wide range of applications related to structured data. PyG is both friendly to machine learning researchers and first-time users of machine learning toolkits.

From PyG 2.3 onwards, you can install and use PyG without any external library required except for PyTorch. For this, simply run: These packages come with their own CPU and GPU kernel implementations based on the PyTorch C++/CUDA/hip (ROCm) extension interface. For a basic usage of PyG, these dependencies are fully optional.

Interpreting GNN models is crucial for many use cases. PyG (2.3 and beyond) provides the torch_geometric.explain package for first-class GNN explainability support that currently includes. and metrics to evaluate explanations via the metric package.

PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data. It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers.

PyG is a library built upon PyTorch to easily write and train Graph Neural Networks for a wide range of applications related to structured data. PyG is both friendly to machine learning researchers and first-time users of machine learning toolkits.

Graph Neural Network Library for PyTorch. PyG has 5 repositories available. Follow their code on GitHub.

Apr 9, 2025 · PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data. It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers.

Sep 12, 2021 · With this, we are releasing PyG 2.0, a new major release that brings sophisticated heterogeneous graph support, GraphGym and many other exciting features to PyG. We finally provide full heterogeneous graph support in PyG 2.0. See here for the accompanying tutorial.