Welcome to Temporal Graph Benchmark

Pip Install

You can install TGB via pip

pip install py-tgb

Links and Datasets

The project website can be found here.

The API documentations can be found here.

all dataset download links can be found at info.py

TGB dataloader will also automatically download the dataset as well as the negative samples for the link property prediction datasets.

Install dependency

Our implementation works with python >= 3.9 and can be installed as follows

set up virtual environment (conda should work as well)

python -m venv ~/tgb_env/
source ~/tgb_env/bin/activate

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

install local dependencies under root directory /TGB
```
pip install -e .
```

Instruction for tracking new documentation and running mkdocs locally

first run the mkdocs server locally in your terminal
```
mkdocs serve
```

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

create docs/api/tgb.hi.md and add the following

# `tgb.edgeregression`

::: tgb.edgeregression.hi

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