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--- |
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license: cc-by-nc-4.0 |
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language: |
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- en |
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tags: |
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- code |
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pretty_name: README |
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--- |
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<!-- # TGB --> |
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**TGB 2.0** |
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Overview of the Temporal Graph Benchmark (TGB) pipeline: |
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- TGB includes large-scale and realistic datasets from five different domains with both dynamic link prediction and node property prediction tasks. |
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- TGB automatically downloads datasets and processes them into `numpy`, `PyTorch` and `PyG compatible TemporalData` formats. |
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- Novel TG models can be easily evaluated on TGB datasets via reproducible and realistic evaluation protocols. |
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- TGB provides public and online leaderboards to track recent developments in temporal graph learning domain. |
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``` |
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pip install py-tgb |
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``` |
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### Links and Datasets |
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The project website can be found [here](https://tgb.complexdatalab.com/). |
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The API documentations can be found [here](https://shenyanghuang.github.io/TGB/). |
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all dataset download links can be found at [info.py](https://github.com/shenyangHuang/TGB/blob/main/tgb/utils/info.py) |
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TGB dataloader will also automatically download the dataset as well as the negative samples for the link property prediction datasets. |
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if website is unaccessible, please use [this link](https://tgb-website.pages.dev/) instead. |
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