ogbn_mag — Microsoft Academic Graph, heterogeneous subset (ogbn-mag, 2020)

Description

A heterogeneous network of papers, their authors, the institutions of the authors, and the fields of study of the papers, a subset of the Microsoft Academic Graph (MAG), from the ogbn-mag dataset of the Open Graph Benchmark (OGB), a benchmark for node classification (version 2, of June 2020, from which duplicate edges were removed). The benchmark task is to predict the venue (conference or journal) of each paper, one of 349 venues, which the papers are labeled with.

The network has nodes of four kinds: 736,389 papers, 1,134,649 authors, 8,740 institutions and 59,965 fields of study, and directed edges of four kinds: an author writes a paper (7,145,660 edges), a paper cites another paper (5,416,271), a paper has_topic a field of study (7,505,078), and an author is affiliated_with an institution (1,043,998). The nodes are identified by their MAG identifiers only (the data do not include titles, names, or the names of the fields of study and venues).

The papers also have the node features of the benchmark: a 128-dimensional vector obtained by averaging the word2vec embeddings of the words in the title and abstract of the paper (the embeddings were trained by the OGB authors on the MAG corpus); the other nodes have no features. The splits of the benchmark (by year) are also left out.

The network has the following properties:

(Node) kind: the kind of the node (paper, author, institution or field_of_study).
(Node) mag_id: the identifier of the node (paper, author, institution or field of study) in the Microsoft Academic Graph.
(Node) year: the publication year of a paper (-1 for other nodes).
(Node) venue: the MAG identifier of the venue (conference or journal) of a paper (-1 for other nodes).
(Node) features: the 128-dimensional feature vector of a paper (the average of the word2vec embeddings of the words of its title and abstract; empty for other nodes).
(Edge) kind: the relation (writes, cites, has_topic or affiliated_with).

Tags

Informational Citation Social Collaboration Academic graph 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/nodeprop/#ogbn-mag
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
ogbn_mag	1,939,743	21,111,007	10.88	591.72	889.06	53.64	-0.03	0.00	6	1.00	Directed	Unipartite	kind mag_id year venue features	kind	335.1 MiB	448.0 MiB	443.3 MiB	512.8 MiB