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_cran()`	Making networks of package dependencies
`read_matrix()` `read_edgelist()` `read_nodelist()` `read_pajek()` `read_ucinet()` `read_dynetml()` `read_graphml()` `read_gml()`	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_explicit()`	Making networks with explicit ties
`create_ego()`	Making ego networks through interviewing
`create_motifs()`	Making motifs
`create_empty()` `create_filled()` `create_ring()` `create_star()` `create_tree()` `create_lattice()` `create_components()` `create_degree()` `create_core()`	Making networks with defined structures
`generate_random()` `generate_configuration()` `generate_man()` `generate_utilities()` `generate_permutation()`	Making unconditional and conditional random networks
`generate_smallworld()` `generate_scalefree()` `generate_fire()` `generate_islands()` `generate_citations()`	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.
Coercion Functions for modifying networks into other classes.
`as_nodelist()` `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_info()`	Modifying network data
`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
Reformatting Functions for reformatting networks, retaining the same network dimensions.
`to_named()` `to_signed()` `to_weighted()`	Modifying network formats
`to_acyclic()` `to_anti()` `to_redirected()` `to_reciprocated()` `to_directed()`	Modifying network formats
`to_unnamed()` `to_undirected()` `to_unweighted()` `to_unsigned()` `to_simplex()` `to_uniplex()`	Modifying network formats
Transforming Functions for transforming networks, which may change the network’s dimensions.
`to_matching()` `to_mentoring()` `to_eulerian()` `to_tree()` `to_dominating()`	Modifying networks paths
`to_ego()` `to_giant()` `to_no_isolates()` `to_subgraph()` `to_blocks()`	Modifying networks scope
Levels Functions for modifying multimodal networks.
`to_onemode()` `to_twomode()` `to_multilevel()`	Modifying network levels
`to_mode1()` `to_mode2()` `to_ties()`	Modifying networks projection
Converting Functions for permuting networks or constructing a network from the nodal correlations of another network.
`to_permuted()`	Network permutation
`to_correlation()` `to_cosine()`	Node correlation
Splits Functions for splitting networks into a list of networks, or (re)joining a list of networks into a single network.
`to_egos()` `to_subgraphs()` `to_components()` `to_waves()` `to_slices()`	Splitting networks into lists
`from_subgraphs()` `from_egos()` `from_waves()` `from_slices()` `from_ties()`	Joining lists of networks, graphs, and matrices
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.
Graphing Functions for graphing networks and plotting results. `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
Laying Out Functions for laying out the nodes in a graph. Included here are some improved or additional layouts to those offered in igraph and ggraph by default.
`layout_tbl_graph_configuration()` `layout_tbl_graph_dyad()` `layout_tbl_graph_triad()` `layout_tbl_graph_tetrad()` `layout_tbl_graph_pentad()` `layout_tbl_graph_hexad()`	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
Theming Functions for tailoring graphs with themes, scales, and palettes.
`set_manynet_theme()` `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.
Network marks `is_*()` functions return a single logical value for the network.
`is_connected()` `is_perfect_matching()` `is_eulerian()` `is_acyclic()` `is_aperiodic()`	Marking networks features
`is_twomode()` `is_weighted()` `is_directed()` `is_labelled()` `is_signed()` `is_complex()` `is_multiplex()` `is_uniplex()` `is_attributed()`	Marking networks formats
`is_manynet()` `is_graph()` `is_edgelist()` `is_list()` `is_longitudinal()` `is_dynamic()`	Marking networks classes
Nodal marks `node_is_*()` functions return a vector of logical values the length of the nodes in the network.
`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_pendant()` `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()`	Marking nodes for selection based on measures
`node_attribute()` `node_names()` `node_is_mode()` `tie_attribute()` `tie_weights()` `tie_signs()`	Describing attributes of nodes or ties in a network
Tie marks `tie_is_*()` functions return a vector of logical values the length of the ties in the network.
`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()` `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_forbidden()` `tie_is_imbalanced()`	Marking ties based on structural 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.
Description
`node_attribute()` `node_names()` `node_is_mode()` `tie_attribute()` `tie_weights()` `tie_signs()`	Describing attributes of nodes or ties in a network
`net_nodes()` `net_ties()` `net_dims()` `net_node_attributes()` `net_tie_attributes()`	Describing network properties
`net_core()` `net_richclub()` `net_factions()` `net_modularity()` `net_smallworld()` `net_scalefree()` `net_balance()`	Measures of network topological features
Centrality
`node_betweenness()` `node_induced()` `node_flow()` `node_stress()` `tie_betweenness()` `net_betweenness()`	Measures of betweenness-like centrality and centralisation
`node_closeness()` `node_harmonic()` `node_reach()` `node_information()` `node_eccentricity()` `node_distance()` `node_vitality()` `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()` `node_leverage()` `tie_degree()` `net_degree()` `net_outdegree()` `net_indegree()`	Measures of degree-like centrality and centralisation
`node_eigenvector()` `node_power()` `node_alpha()` `node_pagerank()` `node_authority()` `node_hub()` `tie_eigenvector()` `net_eigenvector()`	Measures of eigenvector-like centrality and centralisation
`node_bridges()` `node_redundancy()` `node_effsize()` `node_efficiency()` `node_constraint()` `node_hierarchy()` `node_neighbours_degree()` `tie_cohesion()`	Measures of structural holes
`net_connectedness()` `net_efficiency()` `net_upperbound()`	Graph theoretic dimensions of hierarchy
Cohesion
`net_density()` `net_components()` `net_cohesion()` `net_adhesion()` `net_diameter()` `net_length()` `net_independence()`	Measures of network cohesion or connectedness
`net_reciprocity()` `node_reciprocity()` `net_transitivity()` `node_transitivity()` `net_equivalency()` `node_equivalency()` `net_congruency()`	Measures of network closure
`net_richness()` `node_richness()` `net_diversity()` `node_diversity()` `net_heterophily()` `node_heterophily()` `net_assortativity()` `net_spatial()`	Measures of network diversity
Dynamics
`over_membership()` `over_waves()` `over_time()`	Helper functions for measuring over splits of networks
`net_change()` `net_stability()` `net_correlation()`	Measures of network change
`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
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
`node_by_brokerage()` `net_by_brokerage()` `node_brokering_activity()` `node_brokering_exclusivity()`	Motifs of brokerage
`node_by_exposure()`	Motifs of diffusion
`net_by_dyad()` `net_by_triad()` `net_by_tetrad()` `net_by_mixed()`	Motifs at the network level
`node_by_tie()` `node_by_dyad()` `node_by_triad()` `node_by_tetrad()` `node_by_path()`	Motifs at the nodal level
Members
`node_in_brokering()`	Memberships of brokerage
`node_in_roulette()`	Clique partitioning algorithms
`node_in_betweenness()` `node_in_greedy()` `node_in_eigen()` `node_in_walktrap()`	Hierarchical community partitioning algorithms
`node_in_community()` `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_cosine()` `cluster_concor()`	Methods for equivalence clustering
`k_strict()` `k_elbow()` `k_silhouette()`	Methods for selecting clusters
Practicing
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
`gloss()` `print_glossary()` `clear_glossary()`	Adding network glossary items
Data 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
Classic data `ison_` include classic datasets used for introducing and teaching network analysis, though are sometimes quite limited in terms of the attributes of nodes and ties collected.
`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_dolphins`	One-mode, undirected network of frequent associations in a dolphin pod (Lusseau et al. 2003)
`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_marvel_teams` `ison_marvel_relationships`	Multilevel two-mode affiliation, signed one-mode networks of Marvel comic book characters (Yuksel 2017)
`ison_monks`	Multiplex network of 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_southern_women`	Two-mode southern women (Davis, Gardner and Gardner 1941)
`ison_starwars`	Seven one-mode Star Wars character interactions (Gabasova 2016)
Fictional data `fict_` data are popular fictional datasets, treating recognisable themes, and often have more attributes coded.
`fict_friends`	One-mode undirected Friends character scene co-appearances (McNulty, 2020)
`fict_greys`	One-mode undirected network of characters hook-ups on Grey's Anatomy TV show
`fict_lotr`	One-mode network of Lord of the Rings character interactions
`fict_potter`	Six complex one-mode support data in Harry Potter books (Bossaert and Meidert 2013)
`fict_thrones`	One-mode Game of Thrones kinship (Glander 2017)
Political data `irps_` network data concern various international and domestic political networks and problems.
`irps_blogs`	One-mode directed network of links between US political blogs (Adamic and Glance 2005)
`irps_books`	One-mode undirected network of co-purchased books about US politics on Amazon
`irps_usgeo`	One-mode undirected network of US state contiguity (Meghanathan 2017)
`irps_wwi`	One-mode signed network of relationships between European major powers (Antal et al. 2006)

Reference

Making

Importing

Inventing

Playing

Modifying

Coercion

Attributes

Reformatting

Transforming

Levels

Converting

Splits

Missing

Mapping

Graphing

Laying Out

Theming

Marking

Network marks

Nodal marks

Tie marks

Measuring

Description

Centrality

Cohesion

Dynamics

Memberships

Motifs

Members

Methods

Practicing

Learning

Data

Classic data

Fictional data

Political data