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README.md

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----
-license: mit
----
-
-# Dataset Card for ogbg-molhiv
-
-## Table of Contents
-- [Table of Contents](#table-of-contents)
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
-- [External Use](#external-use)
-  - [PyGeometric](#pygeometric)
-- [Dataset Structure](#dataset-structure)
-  - [Data Properties](#data-properties)
-  - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits)
-- [Additional Information](#additional-information)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
-
-## Dataset Description
-
-- **[Homepage](https://ogb.stanford.edu/docs/graphprop/#ogbg-mol)**
-- **[Repository](https://github.com/snap-stanford/ogb):**: 
-- **Paper:**: Open Graph Benchmark: Datasets for Machine Learning on Graphs (see citation) 
-- **Leaderboard:**: [OGB leaderboard](https://ogb.stanford.edu/docs/leader_graphprop/#ogbg-molhiv) and [Papers with code leaderboard](https://paperswithcode.com/sota/graph-property-prediction-on-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](https://ogb.stanford.edu/docs/leader_graphprop/#ogbg-molhiv) and [Papers with code leaderboard](https://paperswithcode.com/sota/graph-property-prediction-on-ogbg-molhiv).
-
-## External Use
-### PyGeometric
-To load in PyGeometric, do the following:
-
-```python
-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: 
-- `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
-```python
-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](https://github.com/clefourrier) for adding this dataset.

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