ogbl_citation2 — Microsoft Academic Graph citations (ogbl-citation2, 2020)

Description

A directed citation network of papers of the Microsoft Academic Graph (MAG), from the ogbl-citation2 dataset of the Open Graph Benchmark (OGB), a benchmark for link prediction (version 1, of December 2020). The papers are a subset of MAG, and an edge from paper A to paper B means that A cites B.

For the benchmark, two references of each of the most recent papers (published in 2019) are held out, one for validation and one for testing (86,596 references each); these held-out citations are included here, so that the network contains all the 30,561,187 citations of the dataset, and they are marked by an edge property.

Each paper also has the node features of the benchmark: a 128-dimensional vector obtained by averaging the word2vec embeddings of the words in its title and abstract (the embeddings were trained by the OGB authors on the MAG corpus). The negative samples of the benchmark (references that do not exist) are also left out. The data do not include the titles of the papers.

The network has the following properties:

(Node) mag_id: the identifier of the paper in the Microsoft Academic Graph.
(Node) year: the publication year of the paper.
(Node) features: the 128-dimensional feature vector of the paper (the average of the word2vec embeddings of the words of its title and abstract).
(Edge) split: the part of the benchmark the citation belongs to: train, valid or test.

Tags

Informational Citation Unweighted Metadata

Added

2026-10-27

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
K. Wang, Z. Shen, C. Huang, C.-H. Wu, Y. Dong, A. Kanakia, "Microsoft Academic Graph: When experts are not enough." Quantitative Science Studies 1(1), 396-413 (2020) 10.1162/qss_a_00021 Sci-Hub

Upstream URL OK

https://ogb.stanford.edu/docs/linkprop/#ogbl-citation2
https://snap.stanford.edu/ogb/data/

Upstream license

ODC-BY 1.0, https://opendatacommons.org/licenses/by/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_citation2	2,927,963	30,561,187	10.44	43.00	193.44	442.64	-0.01	0.06	28	1.00	Directed	Unipartite	mag_id year features	split	1.227 GiB	1.478 GiB	1.467 GiB	1.727 GiB