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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.