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README.md

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-license: cc-by-nc-4.0
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+---
+license: cc-by-nc-4.0
+language:
+- en
+tags:
+- code
+pretty_name: README
+---
+<!-- # TGB -->
+![TGB logo](imgs/logo.png)
+
+**Temporal Graph Benchmark for Machine Learning on Temporal Graphs** (NeurIPS 2023 Datasets and Benchmarks Track)
+<h4>
+	<a href="https://arxiv.org/abs/2307.01026"><img src="https://img.shields.io/badge/arXiv-pdf-yellowgreen"></a>
+	<a href="https://pypi.org/project/py-tgb/"><img src="https://img.shields.io/pypi/v/py-tgb.svg?color=brightgreen"></a>
+	<a href="https://tgb.complexdatalab.com/"><img src="https://img.shields.io/badge/website-blue"></a>
+	<a href="https://docs.tgb.complexdatalab.com/"><img src="https://img.shields.io/badge/docs-orange"></a>
+</h4> 
+
+
+Overview of the Temporal Graph Benchmark (TGB) pipeline:
+- TGB includes large-scale and realistic datasets from five different domains with both dynamic link prediction and node property prediction tasks.
+- TGB automatically downloads datasets and processes them into `numpy`, `PyTorch` and `PyG compatible TemporalData` formats. 
+- Novel TG models can be easily evaluated on TGB datasets via reproducible and realistic evaluation protocols. 
+- TGB provides public and online leaderboards to track recent developments in temporal graph learning domain.
+
+![TGB dataloading and evaluation pipeline](imgs/pipeline.png)
+
+**To submit to [TGB leaderboard](https://tgb.complexdatalab.com/), please fill in this [google form](https://forms.gle/SEsXvN1QHo9tSFwx9)**
+
+**See all version differences and update notes [here](https://tgb.complexdatalab.com/docs/update/)**
+
+### Announcements
+
+**Excited to announce TGX, a companion package for analyzing temporal graphs in WSDM 2024 Demo Track**
+
+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/).
+
+**Excited to announce that TGB has been accepted to NeurIPS 2023 Datasets and Benchmarks Track**
+
+Thanks to everyone for your help in improving TGB! we will continue to improve TGB based on your feedback and suggestions. 
+
+**Please update to version `0.9.2`**
+
+#### version `0.9.2`
+
+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
+
+
+
+#### version `0.9.1`
+
+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. 
+
+
+#### version `0.9.0`
+
+Added the large `tgbn-token` dataset with 72 million edges to the `nodeproppred` dataset. 
+
+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).
+
+
+### Pip Install
+
+You can install TGB via [pip](https://pypi.org/project/py-tgb/). **Requires python >= 3.9**
+```
+pip install py-tgb
+```
+
+### Links and Datasets
+
+The project website can be found [here](https://tgb.complexdatalab.com/).
+
+The API documentations can be found [here](https://shenyanghuang.github.io/TGB/).
+
+all dataset download links can be found at [info.py](https://github.com/shenyangHuang/TGB/blob/main/tgb/utils/info.py)
+
+TGB dataloader will also automatically download the dataset as well as the negative samples for the link property prediction datasets.
+
+if website is unaccessible, please use [this link](https://tgb-website.pages.dev/) instead.
+
+
+### Running Example Methods
+
+- 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.
+- 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.
+- For all other baselines, please see the [TGB_Baselines](https://github.com/fpour/TGB_Baselines) repo.
+
+### Acknowledgments
+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/).
+
+
+### Citation
+
+If code or data from this repo is useful for your project, please consider citing our paper:
+```
+@article{huang2023temporal,
+  title={Temporal graph benchmark for machine learning on temporal graphs},
+  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},
+  journal={Advances in Neural Information Processing Systems},
+  year={2023}
+}
+```
+<!-- 
+
+### Install dependency
+Our implementation works with python >= 3.9 and can be installed as follows
+
+1. set up virtual environment (conda should work as well)
+```
+python -m venv ~/tgb_env/
+source ~/tgb_env/bin/activate
+```
+
+2. install external packages
+```
+pip install pandas==1.5.3
+pip install matplotlib==3.7.1
+pip install clint==0.5.1
+```
+
+install Pytorch and PyG dependencies (needed to run the examples)
+```
+pip install torch==2.0.0 --index-url https://download.pytorch.org/whl/cu117
+pip install torch_geometric==2.3.0
+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
+```
+
+3. install local dependencies under root directory `/TGB`
+```
+pip install -e .
+```
+
+
+### Instruction for tracking new documentation and running mkdocs locally
+
+1. first run the mkdocs server locally in your terminal 
+```
+mkdocs serve
+```
+
+2. go to the local hosted web address similar to
+```
+[14:18:13] Browser connected: http://127.0.0.1:8000/
+```
+
+Example: to track documentation of a new hi.py file in tgb/edgeregression/hi.py
+
+
+3. create docs/api/tgb.hi.md and add the following
+```
+# `tgb.edgeregression`
+
+::: tgb.edgeregression.hi
+```
+
+4. edit mkdocs.yml 
+```
+nav:
+  - Overview: index.md
+  - About: about.md
+  - API:
+	other *.md files 
+	- tgb.edgeregression: api/tgb.hi.md
+```
+
+### Creating new branch ###
+```
+git fetch origin
+
+git checkout -b test origin/test
+```
+
+### dependencies for mkdocs (documentation)
+```
+pip install mkdocs
+pip install mkdocs-material
+pip install mkdocstrings-python
+pip install mkdocs-jupyter
+pip install notebook
+```
+
+
+### full dependency list
+Our implementation works with python >= 3.9 and has the following dependencies
+```
+pytorch == 2.0.0
+torch-geometric == 2.3.0
+torch-scatter==2.1.1
+torch-sparse==0.6.17
+torch-spline-conv==1.2.2
+pandas==1.5.3
+clint==0.5.1
+``` -->