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Notes

  • Installation
    • Installation via Binaries
    • Installation from Source
    • Frequently Asked Questions
  • Introduction by Example
    • Data Handling of Graphs
    • Common Benchmark Datasets
    • Mini-batches
    • Data Transforms
    • Learning Methods on Graphs
  • Creating Message Passing Networks
    • The “MessagePassing” Base Class
    • Implementing the GCN Layer
    • Implementing the Edge Convolution
  • Creating Your Own Datasets
    • Creating “In Memory Datasets”
    • Creating “Larger” Datasets
    • Frequently Asked Questions
  • Advanced Mini-Batching
    • Pairs of Graphs
    • Bipartite Graphs
    • Batching Along New Dimensions
  • Memory-Efficient Aggregations
  • TorchScript Support
    • Converting GNN Models
    • Creating Jittable GNN Operators
  • Colab Notebooks
  • External Resources

Package Reference

  • torch_geometric
  • torch_geometric.nn
    • Convolutional Layers
    • Dense Convolutional Layers
    • Normalization Layers
    • Global Pooling Layers
    • Pooling Layers
    • Dense Pooling Layers
    • Unpooling Layers
    • Models
    • DataParallel Layers
  • torch_geometric.data
  • torch_geometric.datasets
  • torch_geometric.transforms
  • torch_geometric.utils
  • torch_geometric.io
pytorch_geometric
  • »
  • Colab Notebooks
  • Edit on GitHub

Colab Notebooks¶

We have prepared a list of colab notebooks that practically introduces you to the world of Graph Neural Networks with PyTorch Geometric:

  1. Introduction: Hands-on Graph Neural Networks

  2. Node Classification with Graph Neural Networks

  3. Graph Classification with Graph Neural Networks

  4. Scaling Graph Neural Networks

  5. Point Cloud Classification with Graph Neural Networks

  6. Explaining GNN Model Predictions using Captum

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© Copyright 2021, Matthias Fey. Revision d7d8e5e2.

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