Update README.md

README.md

@@ -19,7 +19,6 @@ Overview of the Temporal Graph Benchmark (TGB) pipeline:
 - TGB provides public and online leaderboards to track recent developments in temporal graph learning domain.
 
 
-
 ```
 pip install py-tgb
 ```
@@ -36,116 +35,3 @@ TGB dataloader will also automatically download the dataset as well as the negat
 
 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
-``` -->