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README.md

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+
+# 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)
+- [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 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).
+
+## 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
+```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.