Fast Graph Representation Learning with PyTorch Geometric
This provides a tool for researchers and practitioners working with graph data, but it is incremental as it builds on existing methods and frameworks.
The authors tackled the problem of efficient deep learning on irregular data like graphs by introducing PyTorch Geometric, a library built on PyTorch that achieves high data throughput through GPU acceleration and efficient mini-batch handling.
We introduce PyTorch Geometric, a library for deep learning on irregularly structured input data such as graphs, point clouds and manifolds, built upon PyTorch. In addition to general graph data structures and processing methods, it contains a variety of recently published methods from the domains of relational learning and 3D data processing. PyTorch Geometric achieves high data throughput by leveraging sparse GPU acceleration, by providing dedicated CUDA kernels and by introducing efficient mini-batch handling for input examples of different size. In this work, we present the library in detail and perform a comprehensive comparative study of the implemented methods in homogeneous evaluation scenarios.