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PyTorch Geometric is a new geometric deep learning extension library for PyTorch. With this library, you will be able to perform deep learning on graphs and other irregular graph structures using various methods and features offered by the library.

Additionally, it also offers an easy-to-use mini-batch loader and helpful transforms to perform complex operations. In order to create your own simple interfaces, you can use a range of a large number of datasets offered by PyTorch Geometric library. You can use all these sets of features for performing operations on both arbitrary graphs as well as on 3D meshes or point clouds.

You can find the following list of methods that are currently implemented in the library:

  • SplineConv, Spline based CNNs which are used for irregular structured and geometric input (For eg: Graphs or meshes). You can refer to the research paper for more details.
  • GCNConv provides a scalable approach using semi-supervised learning on graph-structured data. You can refer to the research paper for more details.
  • ChebConv uses a generalized CNN model with fast localized spectral filtering on graphs. You can refer to the research paper for more details.
  • NNConv uses a neural message passing algorithm for Quantum chemistry. You can refer to the research paper for more details.
  • GATConv uses graph attention networks that operate on graph-structured data. You can refer to the research paper for more details.
  • AGNNProp uses attention-based graph neural networks for graph-based semi-supervised learning. You can refer to the research paper for more details.
  • SAGEConv uses representation learning on large graphs thus achieving great results in a variety of prediction tasks. You can refer to the research paper for more details.
  • Graclus Pooling uses weighted graph cuts without Eigenvectors. You can refer to the research paper for more details.
  • Voxel Grid Pooling

In order to learn more about the installation, data handling mechanisms and a full list of implemented methods and datasets, you can refer the documentation. If you want simple hands-on examples to practice you can refer the examples/ directory.

The library is currently in its first Alpha release. You can contribute to the project by raising an issue request if you notice anything unexpected.

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