ogbn_arxiv — arXiv computer science citations (ogbn-arxiv, 1971-2020)
- Description
A directed citation network of the computer science papers of the arXiv preprint server, as indexed by the Microsoft Academic Graph (MAG), from the
ogbn-arxivdataset of the Open Graph Benchmark (OGB), a benchmark for node classification (version 1, of May 2020). Each paper is labeled with its primary arXiv subject area (one of the 40 computer science categories, chosen by the authors and the arXiv moderators), and the publication years range from 1971 to 2020.An edge from paper A to paper B means that A cites B. There are no self-loops, and 8,444 pairs of papers cite each other.
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 titles of the papers come from a separate file of the OGB authors, which also has their abstracts (left out). The titles are as normalized by MAG (in lower case, without punctuation).
The network has the following properties:
mag_id: the identifier of the paper in the Microsoft Academic Graph.title: the title of the paper, normalized.year: the publication year of the paper.subject: the primary arXiv subject area of the paper, e.g.cs.LGorcs.AI.features: the 128-dimensional feature vector of the paper (the average of the word2vec embeddings of the words of its title and abstract).
- Tags
- Informational Citation Unweighted Metadata
- Citation
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- 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
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https://ogb.stanford.edu/docs/nodeprop/#ogbn-arxiv
https://snap.stanford.edu/ogb/data/ - Upstream license
- ODC-BY 1.0, https://opendatacommons.org/licenses/by/1-0/
- Networks
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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
- Ridiculograms*
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