Update README.md

README.md

@@ -1,3 +1,126 @@
 ---
 license: cc0-1.0
 ---
+
+# Dataset Card for ogbg-ppa
+
+## 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-ppa)**
+- **[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-ppa) and [Papers with code leaderboard](https://paperswithcode.com/sota/graph-property-prediction-on-ogbg-ppa)
+
+### Dataset Summary
+
+The `ogbg-ppa` dataset is "a set of undirected protein association neighborhoods extracted from the protein-protein association networks of 1,581 species", over 37 taxonomic groups, by teams at Stanford, to be a part of the Open Graph Benchmark. See their website for dataset postprocessing.
+
+### Supported Tasks and Leaderboards
+
+`ogbg-ppa` should be used for taxonomic group prediction, a 37-way multi-class classification task. The score used is Average Precision on the test set.
+
+## 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
+
+graphs_dataset = load_dataset("graphs-datasets/ogbg-ppa")
+# For the train set (replace by valid or test as needed)
+graphs_list = [Data(graph) for graph in graphs_dataset["train"]]
+graphs_pygeometric = DataLoader(graph_list)
+
+```
+
+
+## Dataset Structure
+
+### Data Properties
+
+| property | value |
+|---|---|
+| scale | small |
+| #graphs | 158,100 |
+| average #nodes | 243.4 |
+| average #edges | 2,266.1 |
+| average node degree | 18.3 | 
+| average cluster coefficient | 0.513 | 
+| MaxSCC ratio | 1.000 |
+| graph diameter | 4.8 |
+
+### Data Fields
+
+Each row of a given file is a graph, with: 
+- `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
+
+The nodes don't have specific features and are implicit from the lists of edges
+
+### 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-ppa')
+
+split_idx = dataset.get_idx_split() 
+train = dataset[split_idx['train']] # valid, test
+```
+
+## Additional Information
+
+### Licensing Information
+The dataset has been released under CC-0 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.