README.md
In netUtils: 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.

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.