TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK
Node Classification	Citeseer	APPNP	Accuracy	70.0 ± 1.4	# 61
Graph Classification	COLLAB	GCN	Accuracy	80.6%	# 11
Node Classification	Cora	APPNP	Accuracy	82.2% ± 1.5%	# 54
Graph Classification	IMDb-B	GIN-0	Accuracy	72.8%	# 28
Graph Classification	MUTAG	GIN-0	Accuracy	85.7%	# 57
Graph Classification	PROTEINS	DiffPool	Accuracy	75.1%	# 56
Node Classification	Pubmed	APPNP	Accuracy	79.4 ± 2.2	# 46
Graph Classification	REDDIT-B	DiffPool	Accuracy	92.1	# 4

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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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Ranked #4 on Graph Classification on REDDIT-B

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Task	Dataset	Model	Metric Name	Metric Value	Global Rank	Benchmark
Node Classification	Citeseer	APPNP	Accuracy	70.0 ± 1.4	# 61	Compare
Graph Classification	COLLAB	GCN	Accuracy	80.6%	# 11	Compare
Node Classification	Cora	APPNP	Accuracy	82.2% ± 1.5%	# 54	Compare
Graph Classification	IMDb-B	GIN-0	Accuracy	72.8%	# 28	Compare
Graph Classification	MUTAG	GIN-0	Accuracy	85.7%	# 57	Compare
Graph Classification	PROTEINS	DiffPool	Accuracy	75.1%	# 56	Compare
Node Classification	Pubmed	APPNP	Accuracy	79.4 ± 2.2	# 46	Compare
Graph Classification	REDDIT-B	DiffPool	Accuracy	92.1	# 4	Compare

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