Install

Due to the nature of the package (only data, no functions), it will not go to CRAN at any point. However, the package is available via drat (If you are looking for stable builds of the package). With drat, you can install and upgrade non-CRAN packages directly from R using the standard install.packages() and update.packages() functions.

# install.packages("drat")
drat::addRepo("schochastics")
install.packages("networkdata")

To save on line of code in the future, you can add drat::addRepo("schochastics") to your .Rprofile.

The developer version is available via github.

remotes::install_github("schochastics/networkdata")

The required space for the package is ~22MB, given that it includes a lot of data.

Overview

So far, the package includes datsets from the following repositories:

• Freeman’s datasets from http://moreno.ss.uci.edu/data including most of the classical (small) datasets in social network analysis.
• The movie networks from https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/T4HBA3 includes interactions among characters in 773 movies which were automatically parsed from scripts.
• Covert networks from https://sites.google.com/site/ucinetsoftware/datasets/covert-networks. Around 50 datasets of networks with different criminal activities (drugs, terrorism, etc.). This dataset was originally compiled by the Mitchell Centre for SNA.
• Animal networks from https://bansallab.github.io/asnr/. A large collection of networks with different interactions (dominance, grooming, etc.) in groups of different species.
• Shakespeare’s plays networks build with data from https://github.com/mallaham/Shakespeare-Plays. Similar to the movie dataset. Includes scene co-appearances of characters in 36 plays of Shakespeare.
• Some networks from http://konect.uni-koblenz.de/. Konect is a large repository of network data. Only a view small datasets are included from there.
• Tennis networks compiled from https://github.com/JeffSackmann (please give credit to him if you use this data). Includes all matches between 1968 and 2019 (both WTA and ATP) as yearly networks. The networks are directed, pointing from loser to winner with the surface (hard, grass, etc) as edge attribute.
• Some random datasets I had lying around (e.g. the “Grey’s Anatomy” hook-up network)

All networks are in igraph format. If you are used to work with the network format (as in sna and ergm), you can use the intergraph package to easily switch between igraph and network.

A list of all datasets can be obtained with

data(package = "networkdata")

Alternatively, use the function show_networks() to get a list of datasets with desired properties.

head(show_networks(type = "directed"),n = 10)
##         variable_name      network_name is_collection no_of_networks
## 38             ants_1            ants_1         FALSE              1
## 39             ants_2            ants_2         FALSE              1
## 42                atp               atp          TRUE             52
## 45             bkfrac            bkfrac         FALSE              1
## 47             bkoffc            bkoffc         FALSE              1
## 49             bktecc            bktecc         FALSE              1
## 50               bott              bott         FALSE              1
## 55           cent_lit          cent_lit         FALSE              1
## 106 dnc_temporalGraph dnc_temporalGraph         FALSE              1
## 109     eies_messages     eies_messages         FALSE              1
##         nodes     edges is_directed is_weighted is_bipartite has_vattr
## 38    16.0000   200.000        TRUE       FALSE        FALSE     FALSE
## 39    13.0000   361.000        TRUE       FALSE        FALSE     FALSE
## 42   499.3462  3164.404        TRUE        TRUE        FALSE      TRUE
## 45    58.0000  3306.000        TRUE        TRUE        FALSE     FALSE
## 47    40.0000  1558.000        TRUE        TRUE        FALSE     FALSE
## 49    34.0000  1122.000        TRUE        TRUE        FALSE     FALSE
## 50    11.0000   256.000        TRUE        TRUE        FALSE      TRUE
## 55   129.0000   613.000        TRUE       FALSE        FALSE     FALSE
## 106 1891.0000 37421.000        TRUE       FALSE        FALSE     FALSE
## 109   32.0000   460.000        TRUE        TRUE        FALSE      TRUE

If you use any of the included datasets, please make sure to cite the appropriate orginal source, which can be found in the help file for each network.