README.md

---
title: README
emoji: 🏢
colorFrom: purple
colorTo: gray
sdk: static
pinned: false
---

The goal of this repository is to store the different graph datasets currently available as benchmarks, to provide them in an homogeneous and easily loadable way. For example, to load them in PyGeometric, you can do the following:
<br><br><small>
```python
from datasets import load_dataset

from torch_geometric.data import Data
from torch_geometric.loader import DataLoader

graphs_dataset = load_dataset("graphs-datasets/<dataset-name>")
graphs_list = [Data(graph) for graph in graphs_dataset["<train/valid/test>"]]
graphs_pygeometric = DataLoader(graph_list)
```
</small>

<br><br><br>
Currently available graphs are:
<br><br>
<strong> Open Graph Benchmark </strong>, graph classification task: <em> ogbg-molhiv, ogbg-molpcba, ogbg-ppa, ogbg-code2 </em> <br>
<strong> Molecular datasets </strong>, graph regression task: <em> ZINC, AQSOL </em>, graph classification task: <em> MUTAG, PROTEINS, AIDS </em> <br>
<strong> MD17 molecular trajectory benchmark </strong>, graph regression task: <em> aspirin, benzene, ethanol, malonaldehyde, naphthalene, salycilic acid, toluene, uracil </em> <br>
<strong> Social datasets </strong>, graph classification task: <em> IMDb-B, twitch_egos, reddit_threads, deezer_ego_nets </em> <br>
<strong> Image datasets </strong>, graph classification task: <em> CIFAR10, MNIST </em> <br>
<strong> Quantum chemistry datasets </strong>, graph classification task: <em> alchemy </em> <br>
<strong> Synthetic dataset </strong>, graph classification task: <em> CSL </em> <br>

- More to come!