Function reference • migraph

Measures Functions for measuring networks and returning a numeric vector or value. Note that all `node_` and `tie_` measures return a single vector so that they can be added directly to graph objects. `network_` measures return one or, in some cases of two-mode measures, two values.
`network_density()` `network_components()` `network_cohesion()` `network_adhesion()` `network_diameter()` `network_length()`	Measures of network cohesion or connectedness
`node_betweenness()` `node_induced()` `node_flow()` `tie_betweenness()` `network_betweenness()`	Measures of betweenness-like centrality and centralisation
`node_closeness()` `node_reach()` `node_harmonic()` `node_information()` `tie_closeness()` `network_closeness()` `network_reach()` `network_harmonic()`	Measures of closeness-like centrality and centralisation
`node_degree()` `node_deg()` `node_outdegree()` `node_indegree()` `node_multidegree()` `node_posneg()` `tie_degree()` `network_degree()` `network_outdegree()` `network_indegree()`	Measures of degree-like centrality and centralisation
`node_eigenvector()` `node_power()` `node_alpha()` `node_pagerank()` `tie_eigenvector()` `network_eigenvector()`	Measures of eigenvector-like centrality and centralisation
`network_reciprocity()` `node_reciprocity()` `network_transitivity()` `node_transitivity()` `network_equivalency()` `network_congruency()`	Measures of network closure
`node_bridges()` `node_redundancy()` `node_effsize()` `node_efficiency()` `node_constraint()` `node_hierarchy()` `node_eccentricity()` `node_neighbours_degree()` `tie_cohesion()`	Measures of structural holes
`network_richness()` `node_richness()` `network_diversity()` `node_diversity()` `network_heterophily()` `node_heterophily()` `network_assortativity()` `network_spatial()`	Measures of network diversity
`network_core()` `network_richclub()` `network_factions()` `network_modularity()` `network_smallworld()` `network_scalefree()` `network_balance()`	Measures of network topological features
`network_change()` `network_stability()`	Measures of network change
`over_waves()` `over_time()`	Helper functions for measuring over splits of networks
`network_connectedness()` `network_efficiency()` `network_upperbound()`	Graph theoretic dimensions of hierarchy
`network_transmissibility()` `network_infection_length()` `network_reproduction()` `network_immunity()` `network_hazard()`	Measures of network diffusion
`node_adoption_time()` `node_adopter()` `node_thresholds()` `node_infection_length()` `node_exposure()`	Measures of nodes in a diffusion
Motifs Functions for calculating subgraphs in multimodal networks. These functions have an additional dimension than `node_` and `network_` measures and marks that capture the different motifs surveyed.
`node_brokerage_census()` `network_brokerage_census()` `node_brokering_activity()` `node_brokering_exclusivity()` `node_brokering()`	Censuses of brokerage motifs
`network_dyad_census()` `network_triad_census()` `network_mixed_census()`	Censuses of motifs at the network level
`node_tie_census()` `node_triad_census()` `node_quad_census()` `node_path_census()`	Censuses of nodes' motifs
Memberships Functions for identifying community, cluster, or class memberships in partitions within multimodal networks. They return integer vectors the length of the nodes in the network.
`node_optimal()` `node_kernighanlin()` `node_edge_betweenness()` `node_fast_greedy()` `node_leading_eigen()` `node_walktrap()` `node_infomap()` `node_spinglass()` `node_fluid()` `node_louvain()` `node_leiden()`	Community partitioning algorithms
`node_components()` `node_weak_components()` `node_strong_components()`	Component partitioning algorithms
`node_equivalence()` `node_structural_equivalence()` `node_regular_equivalence()` `node_automorphic_equivalence()`	Equivalence clustering algorithms
`node_core()` `node_coreness()`	Core-periphery clustering algorithms
`node_roulette()`	Clique partitioning algorithms
Models Functions for modelling multimodal networks:
`test_random()` `test_permutation()` `test_gof()`	Conditional uniform graph and permutation tests
`network_reg()`	Linear and logistic regression for network data
`cluster_hierarchical()` `cluster_concor()`	Methods for equivalence clustering
`k_strict()` `k_elbow()` `k_silhouette()`	Methods for selecting clusters
Data The package contains multimodal, multilevel, and multiplex network data, used in the book Multimodal Political Networks (prefixed with `mpn_`).
`mpn_bristol`	Multimodal (3) Bristol protest events, 1990-2002 (Diani and Bison 2004)
`mpn_cow_trade` `mpn_cow_igo`	One-mode interstate trade relations and two-mode state membership in IGOs (COW)
`mpn_elite_mex`	One-mode Mexican power elite database (Knoke 1990)
`mpn_elite_usa_advice` `mpn_elite_usa_money`	Two-mode and three-mode American power elite database (Domhoff 2016)
`mpn_IT_1990` `mpn_IT_1990` `mpn_IT_2008` `mpn_DE_1990` `mpn_DE_2008` `mpn_UK_1990` `mpn_UK_2008`	Two-mode European Values Survey, 1990 and 2008 (EVS 2020)
`mpn_ryanair`	One-mode EU policy influence network, June 2004 (Christopoulos 2006)
`mpn_DemSxP` `mpn_RepSxP` `mpn_OverSxP`	Two-mode 112th Congress Senate Voting (Knoke et al. 2021)

Reference

Measures

Motifs

Memberships

Models

Data