Datasets:
metadata
license: mit
Dataset Card for AQSOL
Table of Contents
Dataset Description
- Homepage
- Paper:: (see citation)
Dataset Summary
The AQSOL dataset comes "from the Benchmarking Graph Neural Networks paper based on AqSolDB, a standardized database of 9,982 molecular graphs with their aqueous solubility values, collected from 9 different data sources" (PyGeometric doc).
Supported Tasks and Leaderboards
AQSOL
should be used for graph regression, on aqueous solubility.
External Use
PyGeometric
To load in PyGeometric, do the following:
from datasets import load_dataset
from torch_geometric.data import Data
from torch_geometric.loader import DataLoader
dataset_hf = load_dataset("graphs-datasets/<mydataset>")
# For the train set (replace by valid or test as needed)
dataset_pg_list = [Data(graph) for graph in dataset_hf["train"]]
dataset_pg = DataLoader(dataset_pg_list)
Dataset Structure
Data Properties
property | value |
---|---|
#graphs | 9,833 |
average #nodes | 17.6 |
average #edges | 35.8 |
Data Fields
Each row of a given file is a graph, with:
node_feat
(list: #nodes x #node-features): nodesedge_index
(list: 2 x #edges): pairs of nodes constituting edgesedge_attr
(list: #edges x #edge-features): for the aforementioned edges, contains their featuresy
(list: #labels): contains the number of labels available to predictnum_nodes
(int): number of nodes of the graphpos
(list: 2 x #node): positional information of each node
Data Splits
This data is split. It comes from the PyGeometric version of the dataset.
Additional Information
Licensing Information
The dataset has been released under MIT license.
Citation Information
@article{DBLP:journals/corr/abs-2003-00982,
author = {Vijay Prakash Dwivedi and
Chaitanya K. Joshi and
Thomas Laurent and
Yoshua Bengio and
Xavier Bresson},
title = {Benchmarking Graph Neural Networks},
journal = {CoRR},
volume = {abs/2003.00982},
year = {2020},
url = {https://arxiv.org/abs/2003.00982},
eprinttype = {arXiv},
eprint = {2003.00982},
timestamp = {Sat, 23 Jan 2021 01:14:30 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2003-00982.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}