Fast Graph Representation Learning with PyTorch Geometric

6 Mar 2019  ·  Matthias Fey, Jan Eric Lenssen ·

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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Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Node Classification Citeseer APPNP Accuracy 70.0 ± 1.4 # 59
Graph Classification COLLAB GCN Accuracy 80.6% # 11
Node Classification Cora APPNP Accuracy 82.2% ± 1.5% # 53
Graph Classification IMDb-B GIN-0 Accuracy 72.8% # 28
Graph Classification MUTAG GIN-0 Accuracy 85.7% # 61
Graph Classification PROTEINS DiffPool Accuracy 75.1% # 60
Node Classification Pubmed APPNP Accuracy 79.4 ± 2.2 # 46
Graph Classification REDDIT-B DiffPool Accuracy 92.1 # 4

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