Dataset Card for 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:

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

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 for adding this dataset.