github — GitHub contest (2009)
- Description
A bipartite network of users of the software hosting site GitHub and the repositories they watch (i.e. subscribe to), released by GitHub (S. Chacon) for the 2009 GitHub Contest, in which participants had to recommend repositories to users based on the repositories they already watched. The data are an extract of the repository watches in GitHub's database in June 2009, from which a sample of the watches of 4,788 test users was withheld (and is not included). Nodes are users and repositories, and an edge connects a user to a repository that the user watches. Users and repositories are anonymized by numerical ids, but the repositories come with their names, creation dates, fork origin and language composition.
The network has the following properties:
- (Node)
is_user: whether the node is a user (otherwise it is a repository). - (Node)
id: the numerical id of the user or repository in the source data. - (Node)
name(repositories): the repository name ("owner/repository"). - (Node)
created(repositories): its creation date. - (Node)
fork_of(repositories): the name of the repository it was forked from (empty if none). - (Node)
languages,lines(repositories): the programming languages identified in the repository and the number of lines of code in each (empty for the 47,371 repositories without language data).
- (Node)
- Tags
- Economic Preferences Unweighted Metadata
- Added
- 2020-08-11
- Citation
-
- S. Chacon, "The 2009 GitHub Contest." The GitHub Blog (2009) github.blog
- Upstream URL OK
- https://github.blog/news-insights/the-library/the-2009-github-contest/
- Upstream license
- Public domain (as stated in the readme file of the data), https://web.archive.org/web/20220413012321/https://github.s3.amazonaws.com/data/download.zip
- 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
- Ridiculograms*
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