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LRGB: Long Range Graph Benchmark datasets #5935
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Thanks for adding these! Left some initial comments. |
for more information, see https://pre-commit.ci
Thanks @EdisonLeeeee for your comments!
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for more information, see https://pre-commit.ci
Hi @EdisonLeeeee I have updated the code based on the feedbacks -thank you! |
LGTM. Thanks for the update! |
A gentle ping @rusty1s :) |
Co-authored-by: Jintang Li <cnljt@outlook.com>
I'm also wondering if we could add the dataset information (as you summarized in this PR) into docstring so that users can choose specific datasets according to their tasks. FYI: pytorch_geometric/torch_geometric/datasets/md17.py Lines 46 to 51 in 41fd354
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Done, added dataset info in docstring! |
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Thank you for adding <3
This PR adds the LRGB datasets from the paper [Long Range Graph Benchmark](https://openreview.net/pdf?id=in7XC5RcjEn). The original dataset source is [in this repo](http://github.com/vijaydwivedi75/lrgb). The Long Range Graph Benchmark (LRGB) is a collection of 5 graph learning datasets that arguably require long-range reasoning to achieve strong performance in a given task. The 5 datasets in this benchmark can be used to prototype new models that can capture long range dependencies in graphs. Dataset | Domain | Task -- | -- | -- PascalVOC-SP | Computer Vision | Node Classification COCO-SP | Computer Vision | Node Classification PCQM-Contact | Quantum Chemistry | Link Prediction Peptides-func | Chemistry | Graph Classification Peptides-struct | Chemistry | Graph Regression The `torch_geometric.datasets.LRGBDataset` can be used to access any of the 5 datasets in the benchmark. Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Jintang Li <cnljt@outlook.com> Co-authored-by: Jinu Sunil <jinu.sunil@gmail.com> Co-authored-by: Matthias Fey <matthias.fey@tu-dortmund.de>
This PR adds the LRGB datasets from the paper Long Range Graph Benchmark. The original dataset source is in this repo.
The Long Range Graph Benchmark (LRGB) is a collection of 5 graph learning datasets that arguably require long-range reasoning to achieve strong performance in a given task. The 5 datasets in this benchmark can be used to prototype new models that can capture long range dependencies in graphs.
The
torch_geometric.datasets.LRGBDataset
can be used to access any of the 5 datasets in the benchmark.