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README.md
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@@ -19,7 +19,6 @@ Overview of the Temporal Graph Benchmark (TGB) pipeline:
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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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if website is unaccessible, please use [this link](https://tgb-website.pages.dev/) instead.
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### Running Example Methods
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- For the dynamic link property prediction task, see the [`examples/linkproppred`](https://github.com/shenyangHuang/TGB/tree/main/examples/linkproppred) folder for example scripts to run TGN, DyRep and EdgeBank on TGB datasets.
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- For the dynamic node property prediction task, see the [`examples/nodeproppred`](https://github.com/shenyangHuang/TGB/tree/main/examples/nodeproppred) folder for example scripts to run TGN, DyRep and EdgeBank on TGB datasets.
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- For all other baselines, please see the [TGB_Baselines](https://github.com/fpour/TGB_Baselines) repo.
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### Acknowledgments
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We thank the [OGB](https://ogb.stanford.edu/) team for their support throughout this project and sharing their website code for the construction of [TGB website](https://tgb.complexdatalab.com/).
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### Citation
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If code or data from this repo is useful for your project, please consider citing our paper:
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```
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@article{huang2023temporal,
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title={Temporal graph benchmark for machine learning on temporal graphs},
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author={Huang, Shenyang and Poursafaei, Farimah and Danovitch, Jacob and Fey, Matthias and Hu, Weihua and Rossi, Emanuele and Leskovec, Jure and Bronstein, Michael and Rabusseau, Guillaume and Rabbany, Reihaneh},
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journal={Advances in Neural Information Processing Systems},
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year={2023}
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}
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```
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<!--
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### Install dependency
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Our implementation works with python >= 3.9 and can be installed as follows
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1. set up virtual environment (conda should work as well)
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```
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python -m venv ~/tgb_env/
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source ~/tgb_env/bin/activate
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```
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2. install external packages
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```
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pip install pandas==1.5.3
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pip install matplotlib==3.7.1
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pip install clint==0.5.1
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```
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install Pytorch and PyG dependencies (needed to run the examples)
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```
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pip install torch==2.0.0 --index-url https://download.pytorch.org/whl/cu117
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pip install torch_geometric==2.3.0
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pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.0.0+cu117.html
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```
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3. install local dependencies under root directory `/TGB`
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```
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pip install -e .
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```
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### Instruction for tracking new documentation and running mkdocs locally
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1. first run the mkdocs server locally in your terminal
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```
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mkdocs serve
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```
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2. go to the local hosted web address similar to
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```
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[14:18:13] Browser connected: http://127.0.0.1:8000/
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```
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Example: to track documentation of a new hi.py file in tgb/edgeregression/hi.py
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3. create docs/api/tgb.hi.md and add the following
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```
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# `tgb.edgeregression`
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::: tgb.edgeregression.hi
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```
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4. edit mkdocs.yml
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```
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nav:
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- Overview: index.md
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- About: about.md
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- API:
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other *.md files
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- tgb.edgeregression: api/tgb.hi.md
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```
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### Creating new branch ###
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```
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git fetch origin
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git checkout -b test origin/test
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```
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### dependencies for mkdocs (documentation)
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```
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pip install mkdocs
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pip install mkdocs-material
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pip install mkdocstrings-python
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pip install mkdocs-jupyter
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pip install notebook
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```
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### full dependency list
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Our implementation works with python >= 3.9 and has the following dependencies
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```
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pytorch == 2.0.0
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torch-geometric == 2.3.0
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torch-scatter==2.1.1
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torch-sparse==0.6.17
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torch-spline-conv==1.2.2
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pandas==1.5.3
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clint==0.5.1
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``` -->
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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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if website is unaccessible, please use [this link](https://tgb-website.pages.dev/) instead.
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