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