ogbl_ppa — Protein associations of 58 species (ogbl-ppa, 2020)

Description

An undirected network of functional associations between the proteins of 58 species, from the ogbl-ppa dataset of the Open Graph Benchmark (OGB), a benchmark for link prediction (version 1, of May 2020). The proteins and associations are a subset of the STRING database (version 11): an edge means that two proteins are associated in a biologically meaningful way, e.g. by a physical interaction, co-expression, homology or genomic neighborhood (the kinds of evidence are not given). The species span the tree of life (e.g. human, mouse, yeast, Arabidopsis thaliana, Plasmodium falciparum, bacteria). The networks of all the species of STRING are in the entry string_all.

For the benchmark, the edges were split by "throughput": the associations found with high-throughput techniques (e.g. automated screens, or predictions) are used for training, and those found with low-throughput, resource-intensive experiments (e.g. experimentally determined interactions of single protein pairs) for validation and testing. All the 30,326,273 edges of the dataset are included here, with their part of the split as an edge property; the negative samples of the benchmark (pairs of proteins that are not associated) are left out. In the benchmark, each node has as features a one-hot vector of its species, which is given here as the species' taxonomy identifier.

The network has the following properties:

(Node) protein_id: the identifier of the protein in STRING, e.g. 9606.ENSP00000000233 (the NCBI taxonomy identifier of the species, followed by the identifier of the protein in its source database).
(Node) species: the NCBI taxonomy identifier of the species of the protein, e.g. 9606 (human).
(Edge) split: the part of the benchmark the edge belongs to: train, valid or test.

Tags

Biological Protein interactions Unweighted Metadata

Added

2026-10-28

Citation

W. Hu, M. Fey, M. Zitnik, Y. Dong, H. Ren, B. Liu, M. Catasta, J. Leskovec, "Open Graph Benchmark: Datasets for Machine Learning on Graphs." Advances in Neural Information Processing Systems 33, 22118-22133 (2020) 10.48550/arXiv.2005.00687 Sci-Hub
D. Szklarczyk et al., "STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets." Nucleic Acids Research 47, D607-D613 (2019) 10.1093/nar/gky1131 Sci-Hub

Upstream URL OK

https://ogb.stanford.edu/docs/linkprop/#ogbl-ppa
https://snap.stanford.edu/ogb/data/

Upstream license

CC0 1.0, https://creativecommons.org/publicdomain/zero/1.0/

Networks

Tip: hover over a column header for a description.

Column legend

$\left<k\right>$: Mean degree
$\sigma_k$: Standard deviation of the degrees
$\lambda_h$: Largest eigenvalue of the Hashimoto (non-backtracking) matrix
$\tau$: Random walk mixing time
$r$: Degree assortativity
$c$: Global clustering coefficient
$\oslash$: (Pseudo-) diameter
$S$: Relative size of the largest component
NPs: Node properties
EPs: Edge properties

Name	Nodes	Edges	$\left<k\right>$	$\sigma_k$	$\lambda_h$	$\tau$	$r$	$c$	$\oslash$	$S$	Kind	Mode	NPs	EPs	gt	GraphML	GML	csv
ogbl_ppa	576,289	30,326,273	105.25	142.32	916.36	95.38	0.47	0.32	15	1.00	Undirected	Unipartite	protein_id species	split	85.0 MiB	145.3 MiB	140.2 MiB	140.9 MiB