Dataset Card for ogbg-molhiv

Dataset Summary

The ogbg-molhiv dataset is a small molecular property prediction dataset, adapted from MoleculeNet by teams at Stanford, to be a part of the Open Graph Benchmark.

Supported Tasks and Leaderboards

ogbg-molhiv should be used for molecular property prediction (aiming to predict whether molecules inhibit HIV or not), a binary classification task. The score used is ROC-AUC.

The associated leaderboards are here: OGB leaderboard and Papers with code leaderboard.

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

ogbg_molhiv = load_dataset("graphs-datasets/ogbg-molhiv")
# For the train set (replace by valid or test as needed)
ogbg_molhiv_pg_list = [Data(graph) for graph in ogbg_molhiv["train"]]
ogbg_molhiv_pg = DataLoader(ogbg_molhiv_pg_list)

Dataset Structure

Data Properties

property	value
scale	small
#graphs	41,127
average #nodes	25.5
average #edges	27.5
average node degree	2.2
average cluster coefficient	0.002
MaxSCC ratio	0.993
graph diameter	12.0

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: 1 x #labels): contains the number of labels available to predict (here 1, equal to zero or one)
• num_nodes (int): number of nodes of the graph

Data Splits

This data comes from the PyGeometric version of the dataset provided by OGB, and follows the provided data splits. This information can be found back using

from ogb.graphproppred import PygGraphPropPredDataset

dataset = PygGraphPropPredDataset(name = 'ogbg-molhiv')

split_idx = dataset.get_idx_split() 
train = dataset[split_idx['train']] # valid, test

Additional Information

Licensing Information

The dataset has been released under MIT license.

Citation Information

@inproceedings{hu-etal-2020-open,
  author    = {Weihua Hu and
               Matthias Fey and
               Marinka Zitnik and
               Yuxiao Dong and
               Hongyu Ren and
               Bowen Liu and
               Michele Catasta and
               Jure Leskovec},
  editor    = {Hugo Larochelle and
               Marc Aurelio Ranzato and
               Raia Hadsell and
               Maria{-}Florina Balcan and
               Hsuan{-}Tien Lin},
  title     = {Open Graph Benchmark: Datasets for Machine Learning on Graphs},
  booktitle = {Advances in Neural Information Processing Systems 33: Annual Conference
               on Neural Information Processing Systems 2020, NeurIPS 2020, December
               6-12, 2020, virtual},
  year      = {2020},
  url       = {https://proceedings.neurips.cc/paper/2020/hash/fb60d411a5c5b72b2e7d3527cfc84fd0-Abstract.html},
}

Contributions

Thanks to @clefourrier for adding this dataset.