<p align='center'>
<img width='40%' src='https://snap-stanford.github.io/ogb-web/assets/img/OGB_rectangle.png' />
</p>
--------------------------------------------------------------------------------
[](https://pypi.org/project/ogb/)
[](https://github.com/snap-stanford/ogb/blob/master/LICENSE)
## Overview
The Open Graph Benchmark (OGB) is a collection of benchmark datasets, data loaders, and evaluators for graph machine learning. Datasets cover a variety of graph machine learning tasks and real-world applications.
The OGB data loaders are fully compatible with popular graph deep learning frameworks, including [PyTorch Geometric](https://pytorch-geometric.readthedocs.io/en/latest/) and [Deep Graph Library (DGL)](https://www.dgl.ai/). They provide automatic dataset downloading, standardized dataset splits, and unified performance evaluation.
<p align='center'>
<img width='80%' src='https://snap-stanford.github.io/ogb-web/assets/img/ogb_overview.png' />
</p>
OGB aims to provide graph datasets that cover important graph machine learning tasks, diverse dataset scale, and rich domains.
**Graph ML Tasks:** We cover three fundamental graph machine learning tasks: prediction at the level of nodes, links, and graphs.
**Diverse scale:** Small-scale graph datasets can be processed within a single GPU, while medium- and large-scale graphs might require multiple GPUs or clever sampling/partition techniques.
**Rich domains:** Graph datasets come from diverse domains ranging from scientific ones to social/information networks, and also include heterogeneous knowledge graphs.
<p align='center'>
<img width='70%' src='https://snap-stanford.github.io/ogb-web/assets/img/dataset_overview.png' />
</p>
OGB is an on-going effort, and we are planning to increase our coverage in the future.
## Installation
You can install OGB using Python's package manager `pip`.
**If you have previously installed ogb, please make sure you update the version to 1.3.6.**
The release note is available [here](https://github.com/snap-stanford/ogb/releases/tag/1.3.6).
#### Requirements
- Python>=3.6
- PyTorch>=1.6
- DGL>=0.5.0 or torch-geometric>=2.0.2
- Numpy>=1.16.0
- pandas>=0.24.0
- urllib3>=1.24.0
- scikit-learn>=0.20.0
- outdated>=0.2.0
#### Pip install
The recommended way to install OGB is using Python's package manager pip:
```bash
pip install ogb
```
```bash
python -c "import ogb; print(ogb.__version__)"
# This should print "1.3.6". Otherwise, please update the version by
pip install -U ogb
```
#### From source
You can also install OGB from source. This is recommended if you want to contribute to OGB.
```bash
git clone https://github.com/snap-stanford/ogb
cd ogb
pip install -e .
```
## Package Usage
We highlight two key features of OGB, namely, (1) easy-to-use data loaders, and (2) standardized evaluators.
#### (1) Data loaders
We prepare easy-to-use PyTorch Geometric and DGL data loaders. We handle dataset downloading as well as standardized dataset splitting.
Below, on PyTorch Geometric, we see that a few lines of code is sufficient to prepare and split the dataset! Needless to say, you can enjoy the same convenience for DGL!
```python
from ogb.graphproppred import PygGraphPropPredDataset
from torch_geometric.loader import DataLoader
# Download and process data at './dataset/ogbg_molhiv/'
dataset = PygGraphPropPredDataset(name = 'ogbg-molhiv')
split_idx = dataset.get_idx_split()
train_loader = DataLoader(dataset[split_idx['train']], batch_size=32, shuffle=True)
valid_loader = DataLoader(dataset[split_idx['valid']], batch_size=32, shuffle=False)
test_loader = DataLoader(dataset[split_idx['test']], batch_size=32, shuffle=False)
```
#### (2) Evaluators
We also prepare standardized evaluators for easy evaluation and comparison of different methods. The evaluator takes `input_dict` (a dictionary whose format is specified in `evaluator.expected_input_format`) as input, and returns a dictionary storing the performance metric appropriate for the given dataset.
The standardized evaluation protocol allows researchers to reliably compare their methods.
```python
from ogb.graphproppred import Evaluator
evaluator = Evaluator(name = 'ogbg-molhiv')
# You can learn the input and output format specification of the evaluator as follows.
# print(evaluator.expected_input_format)
# print(evaluator.expected_output_format)
input_dict = {'y_true': y_true, 'y_pred': y_pred}
result_dict = evaluator.eval(input_dict) # E.g., {'rocauc': 0.7321}
```
## Citing OGB / OGB-LSC
If you use OGB or [OGB-LSC](https://ogb.stanford.edu/docs/lsc/) datasets in your work, please cite our papers (Bibtex below).
```
@article{hu2020ogb,
title={Open Graph Benchmark: Datasets for Machine Learning on Graphs},
author={Hu, Weihua and Fey, Matthias and Zitnik, Marinka and Dong, Yuxiao and Ren, Hongyu and Liu, Bowen and Catasta, Michele and Leskovec, Jure},
journal={arXiv preprint arXiv:2005.00687},
year={2020}
}
```
```
@article{hu2021ogblsc,
title={OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs},
author={Hu, Weihua and Fey, Matthias and Ren, Hongyu and Nakata, Maho and Dong, Yuxiao and Leskovec, Jure},
journal={arXiv preprint arXiv:2103.09430},
year={2021}
}
```
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"description": "<p align='center'>\n <img width='40%' src='https://snap-stanford.github.io/ogb-web/assets/img/OGB_rectangle.png' />\n</p>\n\n--------------------------------------------------------------------------------\n\n[](https://pypi.org/project/ogb/)\n[](https://github.com/snap-stanford/ogb/blob/master/LICENSE)\n\n## Overview\n\nThe Open Graph Benchmark (OGB) is a collection of benchmark datasets, data loaders, and evaluators for graph machine learning. Datasets cover a variety of graph machine learning tasks and real-world applications.\nThe OGB data loaders are fully compatible with popular graph deep learning frameworks, including [PyTorch Geometric](https://pytorch-geometric.readthedocs.io/en/latest/) and [Deep Graph Library (DGL)](https://www.dgl.ai/). They provide automatic dataset downloading, standardized dataset splits, and unified performance evaluation.\n\n<p align='center'>\n <img width='80%' src='https://snap-stanford.github.io/ogb-web/assets/img/ogb_overview.png' />\n</p>\n\nOGB aims to provide graph datasets that cover important graph machine learning tasks, diverse dataset scale, and rich domains.\n\n**Graph ML Tasks:** We cover three fundamental graph machine learning tasks: prediction at the level of nodes, links, and graphs.\n\n**Diverse scale:** Small-scale graph datasets can be processed within a single GPU, while medium- and large-scale graphs might require multiple GPUs or clever sampling/partition techniques.\n\n**Rich domains:** Graph datasets come from diverse domains ranging from scientific ones to social/information networks, and also include heterogeneous knowledge graphs. \n\n<p align='center'>\n <img width='70%' src='https://snap-stanford.github.io/ogb-web/assets/img/dataset_overview.png' />\n</p>\n\nOGB is an on-going effort, and we are planning to increase our coverage in the future.\n\n## Installation\nYou can install OGB using Python's package manager `pip`.\n**If you have previously installed ogb, please make sure you update the version to 1.3.6.**\nThe release note is available [here](https://github.com/snap-stanford/ogb/releases/tag/1.3.6).\n\n#### Requirements\n - Python>=3.6\n - PyTorch>=1.6\n - DGL>=0.5.0 or torch-geometric>=2.0.2\n - Numpy>=1.16.0\n - pandas>=0.24.0\n - urllib3>=1.24.0\n - scikit-learn>=0.20.0\n - outdated>=0.2.0\n\n#### Pip install\nThe recommended way to install OGB is using Python's package manager pip:\n```bash\npip install ogb\n```\n\n```bash\npython -c \"import ogb; print(ogb.__version__)\"\n# This should print \"1.3.6\". Otherwise, please update the version by\npip install -U ogb\n```\n\n\n#### From source\nYou can also install OGB from source. This is recommended if you want to contribute to OGB.\n```bash\ngit clone https://github.com/snap-stanford/ogb\ncd ogb\npip install -e .\n```\n\n## Package Usage\nWe highlight two key features of OGB, namely, (1) easy-to-use data loaders, and (2) standardized evaluators.\n#### (1) Data loaders\nWe prepare easy-to-use PyTorch Geometric and DGL data loaders. We handle dataset downloading as well as standardized dataset splitting.\nBelow, on PyTorch Geometric, we see that a few lines of code is sufficient to prepare and split the dataset! Needless to say, you can enjoy the same convenience for DGL!\n```python\nfrom ogb.graphproppred import PygGraphPropPredDataset\nfrom torch_geometric.loader import DataLoader\n\n# Download and process data at './dataset/ogbg_molhiv/'\ndataset = PygGraphPropPredDataset(name = 'ogbg-molhiv')\n\nsplit_idx = dataset.get_idx_split() \ntrain_loader = DataLoader(dataset[split_idx['train']], batch_size=32, shuffle=True)\nvalid_loader = DataLoader(dataset[split_idx['valid']], batch_size=32, shuffle=False)\ntest_loader = DataLoader(dataset[split_idx['test']], batch_size=32, shuffle=False)\n```\n\n#### (2) Evaluators\nWe also prepare standardized evaluators for easy evaluation and comparison of different methods. The evaluator takes `input_dict` (a dictionary whose format is specified in `evaluator.expected_input_format`) as input, and returns a dictionary storing the performance metric appropriate for the given dataset.\nThe standardized evaluation protocol allows researchers to reliably compare their methods.\n```python\nfrom ogb.graphproppred import Evaluator\n\nevaluator = Evaluator(name = 'ogbg-molhiv')\n# You can learn the input and output format specification of the evaluator as follows.\n# print(evaluator.expected_input_format) \n# print(evaluator.expected_output_format) \ninput_dict = {'y_true': y_true, 'y_pred': y_pred}\nresult_dict = evaluator.eval(input_dict) # E.g., {'rocauc': 0.7321}\n```\n\n## Citing OGB / OGB-LSC\nIf you use OGB or [OGB-LSC](https://ogb.stanford.edu/docs/lsc/) datasets in your work, please cite our papers (Bibtex below).\n```\n@article{hu2020ogb,\n title={Open Graph Benchmark: Datasets for Machine Learning on Graphs},\n author={Hu, Weihua and Fey, Matthias and Zitnik, Marinka and Dong, Yuxiao and Ren, Hongyu and Liu, Bowen and Catasta, Michele and Leskovec, Jure},\n journal={arXiv preprint arXiv:2005.00687},\n year={2020}\n}\n```\n```\n@article{hu2021ogblsc,\n title={OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs},\n author={Hu, Weihua and Fey, Matthias and Ren, Hongyu and Nakata, Maho and Dong, Yuxiao and Leskovec, Jure},\n journal={arXiv preprint arXiv:2103.09430},\n year={2021}\n}\n```\n\n\n",
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