|
--- |
|
license: cc0-1.0 |
|
task_categories: |
|
- graph-ml |
|
--- |
|
|
|
# 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. |
