Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Benchmark datasets, data loaders, and evaluators for graph machine learning
| Date | Stars |
|---|---|
| 2026-07-31 | 2093 |
| 2026-08-02 | 2093 |
| 2026-08-06 | 2093 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
<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 `evalua
Excerpt of 5,348 characters
Read on GitHub
Weihua Hu
421
Matthias Fey · NVIDIA · Germany
127
25
24
Rishi Puri · NVIDIA · United States
17
12
4
3
Serge Panev · @NVIDIA · United States
3
3
2
2
NVIDIA
2
2
2
youngpill · South Korea
1
1
Hongbo Miao · Archer Aviation | www.archer.com · United States
1
1
Dongkwan Kim · @shen-lab @uilab-kaist @sparcs-kaist
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:2a32cef32835069f, topic:datasets, readme:dataset, desc:datasets
matched fp:2a32cef32835069f, topic:deep-learning