Package: ogb
OverviewThe 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, includingPyTorch GeometricandDeep Graph Library (DGL). They provide automatic dataset downloading, standardized dataset splits, and unified performance evaluation.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.OGB is an on-going effort, and we are planning to increase our coverage in the future.InstallationYou can install OGB using Python's package managerpip.If you have previously installed ogb, please make sure you update the version to 1.3.6.The release note is availablehere.RequirementsPython>=3.6PyTorch>=1.6DGL>=0.5.0 or torch-geometric>=2.0.2Numpy>=1.16.0pandas>=0.24.0urllib3>=1.24.0scikit-learn>=0.20.0outdated>=0.2.0Pip installThe recommended way to install OGB is using Python's package manager pip:pipinstallogbpython-c"import ogb; print(ogb.__version__)"# This should print "1.3.6". Otherwise, please update the version bypipinstall-UogbFrom sourceYou can also install OGB from source. This is recommended if you want to contribute to OGB.gitclonehttps://github.com/snap-stanford/ogbcdogb
pipinstall-e.Package UsageWe highlight two key features of OGB, namely, (1) easy-to-use data loaders, and (2) standardized evaluators.(1) Data loadersWe 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!fromogb.graphproppredimportPygGraphPropPredDatasetfromtorch_geometric.loaderimportDataLoader# 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) EvaluatorsWe also prepare standardized evaluators for easy evaluation and comparison of different methods. The evaluator takesinput_dict(a dictionary whose format is specified inevaluator.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.fromogb.graphproppredimportEvaluatorevaluator=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-LSCIf you use OGB orOGB-LSCdatasets 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}
}