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@@ -18,45 +18,8 @@ Overview of the Temporal Graph Benchmark (TGB) pipeline:
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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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- ![TGB dataloading and evaluation pipeline](imgs/pipeline.png)
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- **To submit to [TGB leaderboard](https://tgb.complexdatalab.com/), please fill in this [google form](https://forms.gle/SEsXvN1QHo9tSFwx9)**
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- **See all version differences and update notes [here](https://tgb.complexdatalab.com/docs/update/)**
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- ### Announcements
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- **Excited to announce TGX, a companion package for analyzing temporal graphs in WSDM 2024 Demo Track**
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- TGX supports all TGB datasets and provides numerous temporal graph visualization plots and statistics out of the box. See our paper: [Temporal Graph Analysis with TGX](https://arxiv.org/abs/2402.03651) and [TGX website](https://complexdata-mila.github.io/TGX/).
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- **Excited to announce that TGB has been accepted to NeurIPS 2023 Datasets and Benchmarks Track**
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- Thanks to everyone for your help in improving TGB! we will continue to improve TGB based on your feedback and suggestions.
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- **Please update to version `0.9.2`**
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- #### version `0.9.2`
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- Update the fix for `tgbl-flight` where now the unix timestamps are provided directly in the dataset. If you had issues with `tgbl-flight`, please remove `TGB/tgb/datasets/tgbl_flight`and redownload the dataset for a clean install
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- #### version `0.9.1`
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- Fixed an issue for `tgbl-flight` where the timestamp conversion is incorrect due to time zone differences. If you had issues with `tgbl-flight` before, please update your package.
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- #### version `0.9.0`
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- Added the large `tgbn-token` dataset with 72 million edges to the `nodeproppred` dataset.
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- Fixed errors in `tgbl-coin` and `tgbl-flight` where a small set of edges are not sorted chronologically. Please update your dataset version for them to version 2 (will be prompted in terminal).
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- ### Pip Install
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- You can install TGB via [pip](https://pypi.org/project/py-tgb/). **Requires python >= 3.9**
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  ```
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  pip install py-tgb
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  ```
 
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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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  ```