Function reference • migraph

Making Functions for constructing, simulating, and importing one-mode and two-mode networks:
`create_empty()` `create_complete()` `create_ring()` `create_star()` `create_tree()` `create_lattice()` `create_components()` `create_core()`	Make networks with defined structures
`generate_random()` `generate_smallworld()` `generate_scalefree()` `generate_permutation()`	Make networks with a stochastic element
`read_matrix()` `read_edgelist()` `write_edgelist()` `read_nodelist()` `write_nodelist()` `read_pajek()` `write_pajek()` `read_ucinet()` `write_ucinet()` `read_dynetml()`	Make networks from/to external formats
Manipulation Functions for coercing objects between different classes, reformatting or transforming the type of network, or adding information to or retrieving information from the network.
`as_edgelist()` `as_matrix()` `as_igraph()` `as_tidygraph()` `as_network()` `as_siena()` `as_graphAM()`	Coercion between migraph-compatible object classes
`to_uniplex()` `to_undirected()` `to_redirected()` `to_unweighted()` `to_unsigned()` `to_unnamed()` `to_named()` `to_simplex()` `to_onemode()` `to_multilevel()` `to_twomode()`	Tools for reformatting networks, graphs, and matrices
`to_egos()` `to_subgraphs()` `to_components()`	Tools for splitting networks, graphs, and matrices
`to_mode1()` `to_mode2()` `to_giant()` `to_subgraph()` `to_ties()` `to_blocks()` `to_matching()` `to_anti()`	Tools for transforming networks, graphs, and matrices
`add_node_attribute()` `add_tie_attribute()` `copy_node_attributes()` `join_ties()`	Adding and copying attributes from one graph to another
`node_names()` `node_mode()` `node_attribute()` `tie_attribute()` `tie_weights()` `tie_signs()` `network_nodes()` `network_ties()` `network_dims()` `network_node_attributes()` `network_tie_attributes()`	Grab various node or edge attributes from a network
Marks Functions for identifying properties of graphs, nodes or ties, all returning logical scalars or vectors. 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.
`is_migraph()` `is_graph()` `is_edgelist()` `is_twomode()` `is_weighted()` `is_directed()` `is_labelled()` `is_signed()` `is_connected()` `is_complex()` `is_multiplex()` `is_uniplex()` `is_acyclic()` `is_aperiodic()` `is_perfect_matching()` `is_eulerian()`	Marking networks based on their properties
`node_is_cutpoint()` `node_is_isolate()` `node_is_core()` `node_is_random()` `node_is_max()` `node_is_min()`	Marking nodes based on their properties
`tie_is_multiple()` `tie_is_loop()` `tie_is_reciprocated()` `tie_is_bridge()` `tie_is_max()` `tie_is_min()`	Marking ties based on their properties
Measures Functions for measuring multimodal networks, all returning numeric scalars or vectors. 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_degree()` `node_closeness()` `node_betweenness()` `node_eigenvector()` `node_reach()` `node_power()`	Measures of node centrality
`tie_degree()` `tie_closeness()` `tie_betweenness()` `tie_eigenvector()`	Measures of tie centrality
`network_degree()` `network_closeness()` `network_betweenness()` `network_eigenvector()`	Measures of network 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()`	Measures of structural holes
`network_richness()` `node_richness()` `network_diversity()` `node_diversity()` `network_homophily()` `node_homophily()` `network_assortativity()`	Measures of network diversity
`network_core()` `network_factions()` `network_modularity()` `network_smallworld()` `network_scalefree()` `network_balance()`	Measures of network topological features
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()`	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_kernighanlin()` `node_walktrap()` `node_edge_betweenness()` `node_fast_greedy()`	Community graph partitioning algorithms
`node_components()` `node_weak_components()` `node_strong_components()` `node_coreness()`	Component partitioning algorithms
`node_equivalence()` `node_structural_equivalence()` `node_regular_equivalence()` `node_automorphic_equivalence()`	Equivalence clustering algorithms
`node_core()`	Core-periphery clustering algorithms
Models Functions for modelling multimodal networks:
`test_random()` `test_permutation()`	Conditional uniform graph and permutation tests
`network_reg()`	Linear and logistic regression for network data
`play_diffusion()` `play_diffusions()` `play_learning()`	Functions to play games on networks
`cluster_hierarchical()` `cluster_concor()`	Methods for equivalence clustering
Mapping Functions for plotting and visualising graphs of different types:
`autographr()` `autographs()`	Quickly graph networks with sensible defaults
`layout_tbl_graph_frgrid()` `layout_tbl_graph_kkgrid()` `layout_tbl_graph_gogrid()` `layout_tbl_graph_stressgrid()`	Layouts for snapping layouts to a grid
`layout_tbl_graph_hierarchy()` `layout_tbl_graph_alluvial()` `layout_tbl_graph_railway()` `layout_tbl_graph_ladder()` `layout_tbl_graph_concentric()`	Layout algorithms based on bi- or other partitions
`gglineage()`	Plot lineage graph
`ggevolution()`	Plot the evolution of a network
Data The package contains two collections of multimodal, multilevel, and multiplex network data, including data used in the book Multimodal Political Networks (prefixed with `mpn_`) and for the course ‘Social Networks Theories and Methods’ (`ison_`).
`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)
`ison_adolescents`	One-mode subset (8 individuals) of the adolescent society (Coleman 1961).
`ison_algebra`	Multiplex graph object of friends, social, and task ties (McFarland 2001)
`ison_brandes` `ison_brandes2`	One-mode and two-mode centrality demonstration networks
`ison_karateka`	One-mode karateka network (Zachary 1977)
`ison_lotr`	One-mode interaction network of Lord of the Rings (book) character interactions
`ison_marvel_teams` `ison_marvel_relationships`	Multilevel two-mode affiliation, signed one-mode networks of Marvel comic book characters (Yüksel 2017)
`ison_networkers`	One-mode EIES dataset (Freeman and Freeman 1979)
`ison_mm` `ison_bm` `ison_mb` `ison_bb`	Two-mode projection examples (Hollway 2021)
`ison_southern_women`	Two-mode southern women (Davis, Gardner and Gardner 1941)

Reference

Making

Manipulation

Marks

Measures

Motifs

Memberships

Models

Mapping

Data