wikipedia_article_networks — Wikipedia article networks on chameleons, crocodiles and squirrels (2018)

Description

Networks of articles of the English Wikipedia on three topics (chameleons, crocodiles and squirrels), collected in December 2018 by B. Rozemberczki and collaborators (MUSAE study) for benchmarks of node embedding and regression methods. Nodes are articles, and an undirected edge connects two articles that link to each other. The article titles were replaced by consecutive indices. Each article has a list of binary features, each marking the presence of an informative noun in its text (only the indices of the features are given, not the nouns), and its average monthly traffic between October 2017 and November 2018, which is the target of the regression task proposed with the data.

There are three networks, chameleon, crocodile and squirrel. The upstream edge lists contain some pairs of articles twice, once in each direction; they are merged into a single undirected edge, which gives the numbers of edges stated by the authors. A few articles are linked to themselves (self-loops), as given in the data.

The networks have the following properties:

traffic: the average monthly traffic of the article (October 2017 to November 2018).
features: the indices of the article's binary (noun) features.

Tags

Informational Web graph Unweighted Metadata

Citation

B. Rozemberczki, C. Allen, and R. Sarkar, "Multi-scale attributed node embedding." Journal of Complex Networks 9(2), cnab014 (2021). 10.1093/comnet/cnab014 Sci-Hub

Upstream URL OK

https://snap.stanford.edu/data/wikipedia-article-networks.html
https://github.com/benedekrozemberczki/datasets

Upstream license

MIT License, https://github.com/benedekrozemberczki/datasets/blob/3b19135105432cbca65765c1113b91c395ed9d44/LICENSE

Networks

Tip: click on a column header to sort the list, or hover over it 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	gt	GraphML	GML	csv
chameleon	2,277	31,421	27.60	46.43	108.55	155.87	-0.20	0.31	11	1.00	Undirected	Unipartite	traffic features	92 KiB	173 KiB	172 KiB	193 KiB
crocodile	11,631	170,918	29.39	107.26	204.30	53.49	-0.28	0.03	11	1.00	Undirected	Unipartite	traffic features	1.3 MiB	2.0 MiB	1.9 MiB	2.7 MiB
squirrel	5,201	198,493	76.33	161.46	396.18	22.54	-0.23	0.35	10	1.00	Undirected	Unipartite	traffic features	273 KiB	758 KiB	759 KiB	824 KiB