metadata

license: mit

Dataset Card for ogbg-molhiv

Table of Contents
Dataset Description
- Dataset Summary
- Supported Tasks and Leaderboards
Dataset Structure
Additional Information

Dataset Description

Homepage
Repository::
Paper:: Open Graph Benchmark: Datasets for Machine Learning on Graphs (see citation)
Leaderboard:: OGB leaderboard and Papers with code leaderboard

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

Dataset Structure

Data Properties

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

Data Fields

Each row of a given file is a graph, with:

x (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.