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  • PyG Documentation — pytorch_geometric documentation - Read the Docs
    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
  • Introduction by Example — pytorch_geometric documentation
    We shortly introduce the fundamental concepts of PyG through self-contained examples For an introduction to Graph Machine Learning, we refer the interested reader to the Stanford CS224W: Machine Learning with Graphs lectures
  • Installation — pytorch_geometric documentation - Read the Docs
    From PyG 2 3 onwards, you can install and use PyG without any external library required except for PyTorch For this, simply run:
  • Colab Notebooks and Video Tutorials — pytorch_geometric documentation
    We have prepared a list of Colab notebooks that practically introduces you to the world of Graph Neural Networks with PyG: Introduction: Hands-on Graph Neural Networks Node Classification with Graph Neural Networks Graph Classification with Graph Neural Networks Scaling Graph Neural Networks Point Cloud Classification with Graph Neural Networks
  • Creating Message Passing Networks — pytorch_geometric documentation
    PyG provides the MessagePassing base class, which helps in creating such kinds of message passing graph neural networks by automatically taking care of message propagation
  • Working with Graph Datasets — pytorch_geometric documentation
    Install PyG Installation; Get Started Introduction by Example; Colab Notebooks and Video Tutorials; Tutorials Design of Graph Neural Networks; Working with Graph Datasets Creating Graph Datasets; Loading Graphs from CSV; Dataset Splitting; Use-Cases Applications; Distributed Training; Advanced Concepts Advanced Mini-Batching; Memory
  • Design of Graph Neural Networks — pytorch_geometric documentation
    Install PyG Installation; Get Started Introduction by Example; Colab Notebooks and Video Tutorials; Tutorials Design of Graph Neural Networks Creating Message Passing Networks; Heterogeneous Graph Learning; Working with Graph Datasets; Use-Cases Applications; Distributed Training; Advanced Concepts Advanced Mini-Batching; Memory
  • Explaining Graph Neural Networks — pytorch_geometric documentation
    PyG (2 3 and beyond) provides the torch_geometric explain package for first-class GNN explainability support that currently includes a flexible interface to generate a variety of explanations via the Explainer class, several underlying explanation algorithms including, e g , GNNExplainer, PGExplainer and CaptumExplainer,


















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