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.
You can install the development version of netUtils with:
# install.packages("remotes")
remotes::install_github("schochastics/netUtils")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.