Package index • netrics

Marking Functions for identifying properties of nodes or ties, all returning logical scalars or vectors.
Nodal marks `node_is_*()` functions return a vector of logical values the length of the nodes in the network.
`node_is_core()`	Marking nodes as core or periphery
`node_is_isolate()` `node_is_pendant()` `node_is_universal()`	Marking nodes based on degree properties
`node_is_latent()` `node_is_infected()` `node_is_recovered()` `node_is_exposed()`	Marking nodes based on diffusion properties
`node_is_independent()` `node_is_cutpoint()` `node_is_fold()` `node_is_mentor()` `node_is_neighbor()`	Marking nodes based on structural properties
`node_is_random()` `node_is_max()` `node_is_min()` `node_is_mean()`	Marking nodes based on measures
Tie marks `tie_is_*()` functions return a vector of logical values the length of the ties in the network.
`tie_is_multiple()` `tie_is_reciprocated()`	Marking ties based on dyadic properties
`tie_is_random()` `tie_is_max()` `tie_is_min()`	Marking ties based on measures
`tie_is_loop()` `tie_is_feedback()` `tie_is_bridge()` `tie_is_path()`	Marking ties based on structural properties
`tie_is_triangular()` `tie_is_transitive()` `tie_is_triplet()` `tie_is_cyclical()` `tie_is_simmelian()` `tie_is_imbalanced()`	Marking ties based on triangular properties
Measuring Functions for measuring networks and returning a numeric vector or value. `net_` measures return one or, in some cases of two-mode measures, two values. All `node_` and `tie_` measures return a single vector, the length of the nodes or ties in the network, respectively.
Centrality
`node_by_betweenness()` `node_by_induced()` `node_by_flow()` `node_by_stress()`	Measuring nodes betweenness-like centrality
`node_by_closeness()` `node_by_harmonic()` `node_by_reach()` `node_by_information()` `node_by_eccentricity()` `node_by_distance()` `node_by_vitality()` `node_by_randomwalk()`	Measuring nodes closeness-like centrality
`node_by_degree()` `node_by_deg()` `node_by_outdegree()` `node_by_indegree()` `node_by_multidegree()` `node_by_posneg()` `node_by_leverage()`	Measuring nodes degree-like centrality
`node_by_eigenvector()` `node_by_power()` `node_by_alpha()` `node_by_pagerank()` `node_by_authority()` `node_by_hub()` `node_by_subgraph()`	Measuring nodes eigenvector-like centrality
`net_by_betweenness()`	Measuring networks betweenness-like centralisation
`net_by_closeness()` `net_by_reach()` `net_by_harmonic()`	Measuring networks closeness-like centralisation
`net_by_degree()` `net_by_outdegree()` `net_by_indegree()`	Measuring networks degree-like centralisation
`net_by_eigenvector()`	Measuring networks eigenvector-like centralisation
`tie_by_betweenness()`	Measuring ties betweenness-like centrality
`tie_by_closeness()`	Measuring ties closeness-like centrality
`tie_by_degree()`	Measuring ties degree-like centrality
`tie_by_eigenvector()`	Measuring ties eigenvector-like centrality
`node_by_bridges()` `node_by_redundancy()` `node_by_effsize()` `node_by_efficiency()` `node_by_constraint()` `node_by_hierarchy()` `node_by_neighbours_degree()`	Measuring nodes brokerage
`tie_by_cohesion()`	Measuring ties brokerage
`node_by_brokering_activity()` `node_by_brokering_exclusivity()`	Measures of brokerage
`net_by_connectedness()` `net_by_efficiency()` `net_by_upperbound()`	Measures of hierarchy
Cohesion
`net_by_density()` `net_by_components()` `net_by_independence()`	Measures of network cohesion
`net_by_reciprocity()` `net_by_transitivity()` `net_by_equivalency()` `net_by_congruency()`	Measuring network closure
`node_by_reciprocity()` `node_by_transitivity()` `node_by_equivalency()`	Measuring node closure
`net_by_core()` `net_by_richclub()` `net_by_factions()` `net_by_modularity()` `net_by_smallworld()` `net_by_scalefree()` `net_by_balance()`	Measuring network topological features
`net_by_cohesion()` `net_by_adhesion()` `net_by_strength()` `net_by_toughness()`	Measures of network fragmentation
`net_by_heterophily()` `net_by_homophily()` `net_by_assortativity()` `net_by_spatial()`	Measures of network assortativity
`node_by_heterophily()` `node_by_homophily()`	Measures of nodes assortativity
`net_by_richness()` `net_by_diversity()`	Measures of network diversity
`node_by_richness()` `node_by_diversity()`	Measures of nodes diversity
`net_by_diameter()` `net_by_length()`	Measures of network breadth
`node_by_kcoreness()` `node_by_coreness()`	Measuring nodes' coreness
Dynamics
`net_by_waves()`	Measures of network change
`net_by_infection_complete()` `net_by_infection_total()` `net_by_infection_peak()`	Measures of network infection
`net_by_transmissibility()` `net_by_recovery()` `net_by_reproduction()` `net_by_immunity()`	Measures of network diffusion
`node_by_adopt_time()` `node_by_adopt_threshold()` `node_by_adopt_recovery()` `node_by_adopt_exposure()`	Measures of nodes in a diffusion
Memberships Motifs are functions for calculating network subgraphs, always return a matrix or table of nodes as rows and motif or other property as columns, and can be recognised by the `_by_` in the function name. Memberships are functions for identifying community, cluster, or class memberships, always return a string vector the length of the nodes in the network, and can be recognised by the `_in_` in the function name.
Motifs
`net_x_brokerage()`	Motifs of network brokerage
`node_x_brokerage()`	Motifs of nodes brokerage
`node_x_exposure()`	Motifs of nodes exposure
`net_x_hazard()`	Motifs of network hazard
`net_x_hierarchy()`	Motifs of network hierarchy
`net_x_dyad()` `net_x_triad()` `net_x_tetrad()` `net_x_mixed()`	Motifs of network cohesion
`node_x_dyad()` `node_x_triad()` `node_x_tetrad()`	Motifs of nodes cohesion
`node_x_tie()` `node_x_path()`	Motifs of nodes pathing
`net_x_change()` `net_x_stability()` `net_x_correlation()`	Motifs of network change
Members
`node_in_brokering()`	Memberships in brokerage positions
`node_in_roulette()`	Memberships in maximally diverse cliques
`node_in_community()`	Memberships in communities
`node_in_betweenness()` `node_in_greedy()` `node_in_eigen()` `node_in_walktrap()`	Memberships in hierarchical communities
`node_in_optimal()` `node_in_partition()` `node_in_infomap()` `node_in_spinglass()` `node_in_fluid()` `node_in_louvain()` `node_in_leiden()`	Memberships in non-hierarchical communities
`node_in_component()` `node_in_weak()` `node_in_strong()`	Memberships in components
`node_in_core()`	Memberships in core-periphery categories
`node_in_adopter()`	Memberships in a diffusion process
`node_in_equivalence()` `node_in_structural()` `node_in_regular()` `node_in_automorphic()`	Memberships in equivalent classes
Methods Methods used in other functions but documented here:
`cluster_hierarchical()` `cluster_cosine()` `cluster_concor()`	Methods for equivalence clustering
`k_strict()` `k_elbow()` `k_silhouette()` `k_gap()`	Methods for selecting clusters

Reference

Marking

Nodal marks

Tie marks

Measuring

Centrality

Cohesion

Dynamics

Memberships

Motifs

Members

Methods