ogbl_biokg — Biomedical knowledge graph BioKG (ogbl-biokg, 2020)

Description

A heterogeneous biomedical knowledge graph, from the ogbl-biokg dataset of the Open Graph Benchmark (OGB), a benchmark for link prediction (version 1, of 2020), built from BioKG, which integrates data of biomedical databases (e.g. UniProt, Reactome, KEGG, DrugBank, SIDER, the Gene Ontology, CTD, ...). The nodes are entities of five types: diseases (10,687), proteins (17,499), drugs (10,533), side effects (9,969) and protein functions (45,085), and each directed edge is a triple (head, relation, tail) with one of 51 relations, e.g. drug-protein interactions, protein-protein interactions of several kinds (activation, binding, catalysis, expression, inhibition, post-translational modification, reaction), drug-side effect associations, drug-drug interactions labeled by the class of diseases on which the interaction has an effect (e.g. drug-drug_cancer), and the hierarchy of protein functions. Two entities can be joined by several edges with different relations.

The triples are split at random into the training (4,762,678), validation (162,886) and test (162,870) sets of the benchmark; the network contains all 5,088,434 triples, with their set as edge property split. The training triples are read from the plain files of the relations, the validation and test triples from the PyTorch files of the split (split/random/valid.pt, test.pt, of a legacy format, read without PyTorch). The negative samples of the validation and test triples (500 random heads and 500 random tails of the same type for each triple, against which the true triple is ranked in the benchmark: about 163 million non-edges) are left out.

The network has the following properties:

(Node) kind: the type of the entity: disease, protein, drug, sideeffect or function.
(Node) entity_id: the identifier of the entity: a UMLS concept identifier for diseases and side effects, an NCBI Gene (Entrez) identifier for proteins, a STITCH compound identifier for drugs, a Gene Ontology identifier for functions.
(Edge) relation: the relation of the triple, e.g. drug-protein, protein-protein_binding.
(Edge) split: the set of the triple in the benchmark: train, valid or test.

Tags

Biological Informational Drug interactions Protein interactions Unweighted Multigraph Metadata

Added

2026-10-31

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
B. Walsh, S. K. Mohamed, V. Nováček, "BioKG: A Knowledge Graph for Relational Learning On Biological Data." Proceedings of the 29th ACM International Conference on Information & Knowledge Management (CIKM), 3173-3180 (2020) 10.1145/3340531.3412776 Sci-Hub

Upstream URL OK

https://ogb.stanford.edu/docs/linkprop/#ogbl-biokg
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_biokg	93,773	5,088,434	54.26	686.06	528.49	143.10	-0.08	0.31	10	1.00	Directed	Unipartite	kind entity_id	relation split	9.2 MiB	21.5 MiB	21.0 MiB	19.9 MiB