A geometry-aware multi-support heterogeneous GNN fuses point, line, and grid rainfall observations via cross-support message passing to reconstruct fields at arbitrary resolutions, reducing RMSE by 23.2% over baselines on Singapore data.
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Fast Graph Representation Learning with PyTorch Geometric
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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.
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