Package index • manynet

Making Making functions help you obtain networks from inside or outside the package.
Importing Functions for importing and exporting networks from and to a range of external formats.
`read_matrix()` `read_edgelist()` `read_nodelist()` `read_pajek()` `read_ucinet()` `read_dynetml()` `read_graphml()`	Making networks from external files
`write_matrix()` `write_edgelist()` `write_nodelist()` `write_pajek()` `write_ucinet()` `write_graphml()`	Making networks to external files
Inventing Functions for deterministically creating and stochastically generating directed and undirected, one-mode and two-mode networks.
`create_empty()` `create_filled()` `create_ring()` `create_star()` `create_tree()` `create_lattice()` `create_components()` `create_core()` `create_explicit()`	Making networks with defined structures
`generate_random()` `generate_smallworld()` `generate_scalefree()` `generate_permutation()` `generate_utilities()` `generate_configuration()`	Making networks with a stochastic element
Playing Functions for simulating diffusion or learning processes, i.e. nodal change, upon networks.
`play_learning()` `play_segregation()`	Making learning models on networks
`play_diffusion()` `play_diffusions()`	Making diffusion models on networks
Modifying Functions for modifying networks into other classes or formats, or modifying their properties or attributes. This includes functions for reformatting networks into networks of the same dimensions but a different type, e.g. from directed to undirected. It also includes functions for transforming networks into networks with other dimensions, such as from a two-mode network into a one-mode network. There are also functions for splitting networks, e.g. into a list of ego networks, and rejoining them from such lists.
`to_correlation()`	Node correlation
`to_uniplex()` `to_undirected()` `to_directed()` `to_redirected()` `to_reciprocated()` `to_acyclic()` `to_unweighted()` `to_unsigned()` `to_unnamed()` `to_named()` `to_simplex()` `to_anti()`	Modifying network formats
`to_egos()` `to_subgraphs()` `to_components()` `to_waves()` `to_slices()`	Splitting networks into lists
`to_onemode()` `to_twomode()` `to_multilevel()`	Modifying network levels
`to_matching()` `to_mentoring()` `to_eulerian()` `to_tree()`	Modifying networks paths
`to_mode1()` `to_mode2()` `to_ties()` `to_galois()`	Modifying networks projection
`to_giant()` `to_no_isolates()` `to_subgraph()` `to_blocks()`	Modifying networks scope
`from_subgraphs()` `from_egos()` `from_waves()` `from_slices()` `from_ties()`	Joining lists of networks, graphs, and matrices
Coercion Functions for modifying networks into other classes.
`as_edgelist()` `as_matrix()` `as_igraph()` `as_tidygraph()` `as_network()` `as_siena()` `as_graphAM()` `as_diffusion()` `as_diffnet()`	Modifying network classes
Attributes Functions for modifying nodal and tie attributes. These include tidy-style and igraph-style functions for adding or joining new data on nodes or ties to networks.
`add_nodes()` `delete_nodes()` `add_node_attribute()` `mutate_nodes()` `mutate()` `bind_node_attributes()` `join_nodes()` `rename_nodes()` `rename()` `filter_nodes()`	Modifying node data
`add_ties()` `delete_ties()` `add_tie_attribute()` `mutate_ties()` `rename_ties()` `arrange_ties()` `bind_ties()` `join_ties()` `filter_ties()` `select_ties()` `summarise_ties()`	Modifying tie data
Missing Functions for modifying how missing data is treated.
`na_to_zero()` `na_to_mean()`	Modifying missing tie data
Mapping Functions for plotting and visualising graphs of different types. `graphr()` graphs any manynet-compatible class object automagically. `graphs()` and `grapht()` do the same for multiple networks and dynamic networks, respectively.
`graphr()`	Easily graph networks with sensible defaults
`graphs()`	Easily graph a set of networks with sensible defaults
`grapht()`	Easily animate dynamic networks with sensible defaults
`layout_tbl_graph_configuration()` `layout_tbl_graph_triad()` `layout_tbl_graph_quad()`	Layout algorithms based on configurational positions
`layout_tbl_graph_hierarchy()` `layout_tbl_graph_alluvial()` `layout_tbl_graph_railway()` `layout_tbl_graph_ladder()` `layout_tbl_graph_concentric()` `layout_tbl_graph_multilevel()` `layout_tbl_graph_lineage()`	Layout algorithms based on bi- or other partitions
`theme_iheid()` `theme_ethz()` `theme_uzh()` `theme_rug()`	Many themes
`scale_fill_iheid()` `scale_colour_iheid()` `scale_color_iheid()` `scale_edge_colour_iheid()` `scale_edge_color_iheid()` `scale_fill_centres()` `scale_colour_centres()` `scale_color_centres()` `scale_edge_colour_centres()` `scale_edge_color_centres()` `scale_fill_sdgs()` `scale_colour_sdgs()` `scale_color_sdgs()` `scale_edge_colour_sdgs()` `scale_edge_color_sdgs()` `scale_fill_ethz()` `scale_colour_ethz()` `scale_color_ethz()` `scale_edge_colour_ethz()` `scale_edge_color_ethz()` `scale_fill_uzh()` `scale_colour_uzh()` `scale_color_uzh()` `scale_edge_colour_uzh()` `scale_edge_color_uzh()` `scale_fill_rug()` `scale_colour_rug()` `scale_color_rug()` `scale_edge_colour_rug()` `scale_edge_color_rug()`	Many scales
`many_palettes()`	Many palettes generator
Marking Functions for identifying properties of networks, 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. `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.
`is_connected()` `is_perfect_matching()` `is_eulerian()` `is_acyclic()` `is_aperiodic()`	Marking networks features
`is_manynet()` `is_graph()` `is_edgelist()` `is_list()` `is_longitudinal()` `is_dynamic()`	Marking networks classes
`is_twomode()` `is_weighted()` `is_directed()` `is_labelled()` `is_signed()` `is_complex()` `is_multiplex()` `is_uniplex()` `is_attributed()`	Marking networks formats
`node_is_core()` `node_coreness()`	Core-periphery clustering algorithms
`node_is_latent()` `node_is_infected()` `node_is_recovered()` `node_is_exposed()`	Marking nodes based on diffusion properties
`node_is_isolate()` `node_is_independent()` `node_is_cutpoint()` `node_is_fold()` `node_is_mentor()`	Marking nodes based on structural properties
`node_is_random()` `node_is_max()` `node_is_min()`	Marking nodes for selection based on measures
`tie_is_random()` `tie_is_max()` `tie_is_min()`	Marking ties for selection based on measures
`tie_is_multiple()` `tie_is_loop()` `tie_is_reciprocated()` `tie_is_feedback()` `tie_is_bridge()`	Marking ties based on structural properties
`node_attribute()` `node_names()` `node_is_mode()` `tie_attribute()` `tie_weights()` `tie_signs()`	Describing attributes of nodes or ties in a network
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.
`node_attribute()` `node_names()` `node_is_mode()` `tie_attribute()` `tie_weights()` `tie_signs()`	Describing attributes of nodes or ties in a network
`net_reciprocity()` `node_reciprocity()` `net_transitivity()` `node_transitivity()` `net_equivalency()` `net_congruency()`	Measures of network closure
`net_density()` `net_components()` `net_cohesion()` `net_adhesion()` `net_diameter()` `net_length()` `net_independence()`	Measures of network cohesion or connectedness
`net_core()` `net_richclub()` `net_factions()` `net_modularity()` `net_smallworld()` `net_scalefree()` `net_balance()`	Measures of network topological features
`net_richness()` `node_richness()` `net_diversity()` `node_diversity()` `net_heterophily()` `node_heterophily()` `net_assortativity()` `net_spatial()`	Measures of network diversity
`net_connectedness()` `net_efficiency()` `net_upperbound()`	Graph theoretic dimensions of hierarchy
`node_bridges()` `node_redundancy()` `node_effsize()` `node_efficiency()` `node_constraint()` `node_hierarchy()` `node_eccentricity()` `node_neighbours_degree()` `tie_cohesion()`	Measures of structural holes
`net_infection_complete()` `net_infection_total()` `net_infection_peak()`	Measures of network infection
`net_transmissibility()` `net_recovery()` `net_reproduction()` `net_immunity()` `net_hazard()`	Measures of network diffusion
`node_adoption_time()` `node_thresholds()` `node_recovery()` `node_exposure()`	Measures of nodes in a diffusion
`net_change()` `net_stability()`	Measures of network change
`net_nodes()` `net_ties()` `net_dims()` `net_node_attributes()` `net_tie_attributes()`	Describing network properties
`node_betweenness()` `node_induced()` `node_flow()` `tie_betweenness()` `net_betweenness()`	Measures of betweenness-like centrality and centralisation
`node_closeness()` `node_reach()` `node_harmonic()` `node_information()` `tie_closeness()` `net_closeness()` `net_reach()` `net_harmonic()`	Measures of closeness-like centrality and centralisation
`node_degree()` `node_deg()` `node_outdegree()` `node_indegree()` `node_multidegree()` `node_posneg()` `tie_degree()` `net_degree()` `net_outdegree()` `net_indegree()`	Measures of degree-like centrality and centralisation
`node_eigenvector()` `node_power()` `node_alpha()` `node_pagerank()` `tie_eigenvector()` `net_eigenvector()`	Measures of eigenvector-like centrality and centralisation
`over_waves()` `over_time()`	Helper functions for measuring over splits of networks
Motifs and 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.
`node_by_brokerage()` `net_by_brokerage()` `node_brokering_activity()` `node_brokering_exclusivity()` `node_in_brokering()`	Motifs of brokerage
`node_by_exposure()`	Motifs of diffusion
`net_by_dyad()` `net_by_triad()` `net_by_mixed()`	Motifs at the network level
`node_by_tie()` `node_by_triad()` `node_by_quad()` `node_by_path()`	Motifs at the nodal level
`node_in_roulette()`	Clique partitioning algorithms
`node_in_betweenness()` `node_in_greedy()` `node_in_eigen()` `node_in_walktrap()`	Hierarchical community partitioning algorithms
`node_in_optimal()` `node_in_partition()` `node_in_infomap()` `node_in_spinglass()` `node_in_fluid()` `node_in_louvain()` `node_in_leiden()`	Non-hierarchical community partitioning algorithms
`node_in_component()` `node_in_weak()` `node_in_strong()`	Component partitioning algorithms
`node_in_adopter()`	Membership of nodes in a diffusion
`node_in_equivalence()` `node_in_structural()` `node_in_regular()` `node_in_automorphic()`	Equivalence clustering algorithms
Methods Methods used in other functions but documented here:
`cluster_hierarchical()` `cluster_concor()`	Methods for equivalence clustering
`k_strict()` `k_elbow()` `k_silhouette()`	Methods for selecting clusters
Learning Functions for loading and extracting code from tutorials. There is also a function for tabulating the various network data included in the package, providing an overview of their properties.
`run_tute()` `extract_tute()`	Open and extract code from tutorials
Practicing The package contains a variety of networks useful for pedagogical purposes and used in the course ‘Social Networks Theories and Methods’ and other workshops. Each page documents the source of the data and its format. References are provided for further reading and citation.
`table_data()`	Obtain overview of available network data
`ison_adolescents`	One-mode subset of the adolescent society network (Coleman 1961)
`ison_algebra`	Multiplex graph object of friends, social, and task ties (McFarland 2001)
`ison_brandes`	One-mode and two-mode centrality demonstration networks
`ison_friends`	One-mode Friends character connections (McNulty, 2020)
`ison_greys`	One-mode undirected network of characters hook-ups on Grey's Anatomy TV show
`ison_hightech`	One-mode multiplex, directed network of managers of a high-tech company (Krackhardt 1987)
`ison_karateka`	One-mode karateka network (Zachary 1977)
`ison_koenigsberg`	One-mode Seven Bridges of Koenigsberg network (Euler 1741)
`ison_laterals`	Two-mode projection examples (Hollway 2021)
`ison_lawfirm`	One-mode lawfirm (Lazega 2001)
`ison_lotr`	One-mode network of Lord of the Rings character interactions
`ison_marvel_teams` `ison_marvel_relationships`	Multilevel two-mode affiliation, signed one-mode networks of Marvel comic book characters (Yuksel 2017)
`ison_monastery_like` `ison_monastery_esteem` `ison_monastery_influence` `ison_monastery_praise`	Three one-mode signed, weighted networks and a three-wave longitudinal network of monks (Sampson 1969)
`ison_networkers`	One-mode EIES dataset (Freeman and Freeman 1979)
`ison_physicians`	Four multiplex one-mode physician diffusion data (Coleman, Katz, and Menzel, 1966)
`ison_potter`	Six complex one-mode support data in Harry Potter books (Bossaert and Meidert 2013)
`ison_southern_women`	Two-mode southern women (Davis, Gardner and Gardner 1941)
`ison_starwars`	Seven one-mode Star Wars character interactions (Gabasova 2016)
`ison_usstates`	One-mode undirected network of US state contiguity (Meghanathan 2017)

Reference

Making

Importing

Inventing

Playing

Modifying

Coercion

Attributes

Missing

Mapping

Marking

Measuring

Motifs and Memberships

Methods

Learning

Practicing