A Collection of Tools for Network Analysis.
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 functionsbiggest_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.
methodsdyad_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.