wikics — Wikipedia computer science articles (Wiki-CS, 2019)
- Description
A network of hyperlinks between the articles of the English Wikipedia about computer science, from the Wiki-CS dataset, a benchmark for node classification. The articles were selected from the Wikipedia dumps of August 2019, based on their categories, which were aggregated to ten branches of computer science (e.g. "Operating systems" or "Computer security") with the category sanitizer of Boldi and Monti (2016), and then cleaned up by hand; articles that would have been assigned to several branches were dropped. Each article is labeled with its branch.
A directed edge from article A to article B means that A links to B. The links are as given by the dataset (in which the benchmark treats them as undirected): an article may link to another one more than once (6,071 repeated links, giving parallel edges), and 520 articles link to themselves.
The node features of the benchmark (averages of the GloVe embeddings of the words of the articles), and the words of the articles themselves, are left out, as are the train, validation and test splits.
The network has the following properties:
title: the title of the article.wikipedia_id: the page identifier of the article in the English Wikipedia.branch: the branch of computer science of the article, e.g.Computer architecture.
- Tags
- Informational Web graph Unweighted Multigraph Metadata
- Citation
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- P. Mernyei, C. Cangea, "Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks." Graph Representation Learning and Beyond workshop, ICML (2020) 10.48550/arXiv.2007.02901 Sci-Hub
- Upstream URL OK
- https://github.com/pmernyei/wiki-cs-dataset
- Upstream license
- MIT, https://github.com/pmernyei/wiki-cs-dataset/blob/f5207315d649377f936edb66d7d93f5342f01d81/LICENSE
- 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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