README.md

metadata

license: mit

Dataset Card for AQSOL

Table of Contents

• Table of Contents
• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
• External Use
  • PyGeometric
• Dataset Structure
  • Data Properties
  • Data Fields
  • Data Splits
• Additional Information
  • Licensing Information
  • Citation Information
  • Contributions

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): nodes
• edge_index (list: 2 x #edges): pairs of nodes constituting edges
• edge_attr (list: #edges x #edge-features): for the aforementioned edges, contains their features
• y (list: #labels): contains the number of labels available to predict
• num_nodes (int): number of nodes of the graph
• pos (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}
}