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license: mit |
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# Dataset Card for ogbg-molhiv |
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## Table of Contents |
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- [Table of Contents](#table-of-contents) |
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- [Dataset Description](#dataset-description) |
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- [Dataset Summary](#dataset-summary) |
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- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) |
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- [Dataset Structure](#dataset-structure) |
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- [Data Properties](#data-properties) |
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- [Data Fields](#data-fields) |
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- [Data Splits](#data-splits) |
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- [Additional Information](#additional-information) |
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- [Licensing Information](#licensing-information) |
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- [Citation Information](#citation-information) |
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- [Contributions](#contributions) |
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## Dataset Description |
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- **[Homepage](https://ogb.stanford.edu/docs/graphprop/#ogbg-mol)** |
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- **[Repository](https://github.com/snap-stanford/ogb):**: |
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- **Paper:**: Open Graph Benchmark: Datasets for Machine Learning on Graphs (see citation) |
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- **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) |
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### Dataset Summary |
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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. |
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### Supported Tasks and Leaderboards |
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`ogbg-molhiv` should be used for molecular property prediction (aiming to predict whether molecules inhibit HIV or not), a binary classification task. |
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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). |
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## Dataset Structure |
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### Data Properties |
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| scale | #graphs | average #nodes | average #edges | |
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| small | 41,127 | 25.5 | 27.5 | |
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| average node degree | average cluster coefficient | MaxSCC ratio | graph diameter | |
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| 2.2 | 0.002 | 0.993 | 12.0 | |
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### Data Fields |
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Each row of a given file is a graph, with: |
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- `x` (list: #nodes x #node-features): nodes |
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- `edge_index` (list: 2 x #edges): pairs of nodes constituting edges |
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- `edge_attr` (list: #edges x #edge-features): for the aforementioned edges, contains their features |
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- `y` (list: 1 x #labels): contains the number of labels available to predict (here 1, equal to zero or one) |
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- `num_nodes` (int): number of nodes of the graph |
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### Data Splits |
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This data comes from the PyGeometric version of the dataset provided by OGB, and follows the provided data splits. |
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This information can be found back using |
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```python |
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from ogb.graphproppred import PygGraphPropPredDataset |
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dataset = PygGraphPropPredDataset(name = 'ogbg-molhiv') |
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split_idx = dataset.get_idx_split() |
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train = dataset[split_idx['train']] # valid, test |
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``` |
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## Additional Information |
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### Licensing Information |
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The dataset has been released under MIT license. |
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### Citation Information |
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``` |
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@inproceedings{hu-etal-2020-open, |
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author = {Weihua Hu and |
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Matthias Fey and |
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Marinka Zitnik and |
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Yuxiao Dong and |
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Hongyu Ren and |
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Bowen Liu and |
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Michele Catasta and |
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Jure Leskovec}, |
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editor = {Hugo Larochelle and |
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Marc Aurelio Ranzato and |
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Raia Hadsell and |
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Maria{-}Florina Balcan and |
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Hsuan{-}Tien Lin}, |
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title = {Open Graph Benchmark: Datasets for Machine Learning on Graphs}, |
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booktitle = {Advances in Neural Information Processing Systems 33: Annual Conference |
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on Neural Information Processing Systems 2020, NeurIPS 2020, December |
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6-12, 2020, virtual}, |
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year = {2020}, |
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url = {https://proceedings.neurips.cc/paper/2020/hash/fb60d411a5c5b72b2e7d3527cfc84fd0-Abstract.html}, |
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} |
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``` |
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### Contributions |
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Thanks to [@clefourrier](https://github.com/clefourrier) for adding this dataset. |
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