@article{
 title = {Fast Graph Representation Learning with PyTorch Geometric},
 type = {article},
 year = {2019},
 pages = {1-9},
 websites = {http://arxiv.org/abs/1903.02428},
 id = {6950ec41-6cc4-37fa-b335-e6caff7eb11c},
 created = {2022-02-21T08:54:45.173Z},
 file_attached = {true},
 profile_id = {f3d36c73-062b-3738-9a74-d09e4e83eb1e},
 group_id = {1ff583c0-be37-34fa-9c04-73c69437d354},
 last_modified = {2022-02-21T08:54:47.353Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 folder_uuids = {e523c16b-0594-4b52-9c4e-9052fcb9dbed},
 private_publication = {false},
 abstract = {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.},
 bibtype = {article},
 author = {Fey, Matthias and Lenssen, Jan Eric},
 number = {1}
}

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