The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

netUtils

netUtils is a collection of tools for network analysis that may not deserve a package on their own and/or are missing from other network packages.

Installation

You can install the development version of netUtils with:

# install.packages("remotes")
remotes::install_github("schochastics/netUtils")

Functions

most functions only support igraph objects

helper/convenience functions
biggest_component() extracts the biggest connected component of a network.
delete_isolates() deletes vertices with degree zero.
bipartite_from_data_frame() creates a two mode network from a data frame.
graph_from_multi_edgelist() creates multiple graphs from a typed edgelist.
clique_vertex_mat() computes the clique vertex matrix.
graph_cartesian() computes the Cartesian product of two graphs.
graph_direct() computes the direct (or tensor) product of graphs.
str() extends str to work with igraph objects.

methods
dyad_census_attr() calculates dyad census with node attributes.
triad_census_attr() calculates triad census with node attributes.
core_periphery() fits a discrete core periphery model.
graph_kpartite() creates a random k-partite network.
split_graph() sample graph with perfect core periphery structure.
sample_coreseq() creates a random graph with given coreness sequence.
sample_pa_homophilic() creates a preferential attachment graph with two groups of nodes.
sample_lfr() create LFR benchmark graph for community detection.
structural_equivalence() finds structurally equivalent vertices.
reciprocity_cor() reciprocity as a correlation coefficient.

methods to use with caution
(this functions should only be used if you know what you are doing)
as_adj_list1() extracts the adjacency list faster, but less stable, from igraph objects.
as_adj_weighted() extracts the dense weighted adjacency matrix fast.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.