Package index • tidygraph

New verbs While tidygraph mainly works by allowing you to use well known dplyr verbs on relational data, it provides a few new verbs special to working with this type of data.
`activate()` `active()` `%N>%` `%E>%`	Determine the context of subsequent manipulations
`bind_graphs()` `bind_nodes()` `bind_edges()`	Add graphs, nodes, or edges to a tbl_graph
`graph_join()`	Join graphs on common nodes
`reroute()`	Change terminal nodes of edges
`focus()` `unfocus()`	Select specific nodes or edges to compute on
`iterate_n()` `iterate_while()`	Repeatedly modify a graph by a function
`morph()` `unmorph()` `crystallise()` `crystallize()` `convert()`	Create a temporary alternative representation of the graph to compute on
`to_linegraph()` `to_subgraph()` `to_subcomponent()` `to_split()` `to_components()` `to_largest_component()` `to_complement()` `to_local_neighborhood()` `to_dominator_tree()` `to_minimum_spanning_tree()` `to_random_spanning_tree()` `to_shortest_path()` `to_bfs_tree()` `to_dfs_tree()` `to_simple()` `to_contracted()` `to_unfolded_tree()` `to_directed()` `to_undirected()` `to_hierarchical_clusters()`	Functions to generate alternate representations of graphs
Graph creation Graphs and networks can come from many sources, or be created by simulation or deterministacally. Tidygraph provides conversions from all well-known structures in R, as well as a range of `create_()` and `play_*()` functions for creating well-defined or simulated graphs.
`tbl_graph()` `as_tbl_graph()` `is.tbl_graph()`	A data structure for tidy graph manipulation
`create_ring()` `create_path()` `create_chordal_ring()` `create_de_bruijn()` `create_empty()` `create_bipartite()` `create_citation()` `create_complete()` `create_notable()` `create_kautz()` `create_lattice()` `create_star()` `create_tree()`	Create different types of well-defined graphs
`play_degree()` `play_dotprod()` `play_fitness()` `play_fitness_power()` `play_gnm()` `play_gnp()` `play_geometry()` `play_erdos_renyi()`	Graph games based on direct sampling
`play_blocks()` `play_blocks_hierarchy()` `play_islands()` `play_smallworld()`	Graph games based on connected components
`play_citation_age()` `play_forestfire()` `play_growing()` `play_barabasi_albert()` `play_barabasi_albert_aging()`	Graph games based on evolution
`play_preference()` `play_preference_asym()` `play_bipartite()` `play_traits()` `play_citation_type()`	Graph games based on different node types
Mapping over nodes Mapping functions over nodes for relational data is different than for standard tabular data as you often want to recurse over a search or in other ways use the graph structure as part of your map.
`map_bfs()` `map_bfs_lgl()` `map_bfs_chr()` `map_bfs_int()` `map_bfs_dbl()`	Apply a function to nodes in the order of a breath first search
`map_bfs_back()` `map_bfs_back_lgl()` `map_bfs_back_chr()` `map_bfs_back_int()` `map_bfs_back_dbl()`	Apply a function to nodes in the reverse order of a breath first search
`map_dfs()` `map_dfs_lgl()` `map_dfs_chr()` `map_dfs_int()` `map_dfs_dbl()`	Apply a function to nodes in the order of a depth first search
`map_dfs_back()` `map_dfs_back_lgl()` `map_dfs_back_chr()` `map_dfs_back_int()` `map_dfs_back_dbl()`	Apply a function to nodes in the reverse order of a depth first search
`map_local()` `map_local_lgl()` `map_local_chr()` `map_local_int()` `map_local_dbl()`	Map a function over a graph representing the neighborhood of each node
Searches Searching is central to many graph algorithms and is often performed in either a breath-first, or depth-first manner. Tidygraph provides a slew of functions for extracting out information from such searches.
`bfs_rank()` `bfs_parent()` `bfs_before()` `bfs_after()` `bfs_dist()` `dfs_rank()` `dfs_rank_out()` `dfs_parent()` `dfs_dist()`	Search a graph with depth first and breath first
Centrality Centrality is a key measure in network analysis, and while it is mainly calculated for nodes, a few algorithms exists for calculating edge centrality as well.
`centrality_alpha()` `centrality_authority()` `centrality_betweenness()` `centrality_power()` `centrality_closeness()` `centrality_eigen()` `centrality_hub()` `centrality_pagerank()` `centrality_subgraph()` `centrality_degree()` `centrality_edge_betweenness()` `centrality_harmonic()` `centrality_manual()` `centrality_closeness_harmonic()` `centrality_closeness_residual()` `centrality_closeness_generalised()` `centrality_integration()` `centrality_communicability()` `centrality_communicability_odd()` `centrality_communicability_even()` `centrality_subgraph_odd()` `centrality_subgraph_even()` `centrality_katz()` `centrality_betweenness_network()` `centrality_betweenness_current()` `centrality_betweenness_communicability()` `centrality_betweenness_rsp_simple()` `centrality_betweenness_rsp_net()` `centrality_information()` `centrality_decay()` `centrality_random_walk()` `centrality_expected()`	Calculate node and edge centrality
Community detection Social network analysis is especially interested in detecting groups or communities within a graph, but such algorithms are also useful in other areas of network research. No single algorithm can provide the correct grouping of nodes so several exists that weigh certain features differently.
`group_components()` `group_edge_betweenness()` `group_fast_greedy()` `group_infomap()` `group_label_prop()` `group_leading_eigen()` `group_louvain()` `group_leiden()` `group_optimal()` `group_spinglass()` `group_walktrap()` `group_fluid()` `group_biconnected_component()` `group_color()`	Group nodes and edges based on community structure
Node measures Tidygraph provides a wide range of algorithms for extracting differnt information about the nodes in a graph, from calculating local measures, to defining whether they are part of certain topological features.
`local_size()` `local_members()` `local_triangles()` `local_ave_degree()` `local_transitivity()`	Measures based on the neighborhood of each node
`node_eccentricity()` `node_constraint()` `node_coreness()` `node_diversity()` `node_efficiency()` `node_bridging_score()` `node_effective_network_size()` `node_connectivity_impact()` `node_closeness_impact()` `node_fareness_impact()`	Querying node measures
`node_rank_hclust()` `node_rank_anneal()` `node_rank_branch_bound()` `node_rank_traveller()` `node_rank_two()` `node_rank_mds()` `node_rank_leafsort()` `node_rank_visual()` `node_rank_spectral()` `node_rank_spin_out()` `node_rank_spin_in()` `node_rank_quadratic()` `node_rank_genetic()` `node_rank_dendser()`	Calculate node ranking
`node_dominator()` `node_topo_order()`	Node properties related to the graph topology
`node_is_cut()` `node_is_root()` `node_is_leaf()` `node_is_sink()` `node_is_source()` `node_is_isolated()` `node_is_universal()` `node_is_simplical()` `node_is_center()` `node_is_adjacent()` `node_is_keyplayer()` `node_is_connected()`	Querying node types
`node_adhesion_to()` `node_adhesion_from()` `node_cohesion_to()` `node_cohesion_from()` `node_distance_to()` `node_distance_from()` `node_cocitation_with()` `node_bibcoupling_with()` `node_similarity_with()` `node_max_flow_to()` `node_max_flow_from()`	Calculate node pair properties
`random_walk_rank()`	Perform a random walk on the graph and return encounter rank
Edge measures Fewer quantitative measures exist for edges, but they can still be queried for topological features such as which nodes they link, etc.
`edge_is_multiple()` `edge_is_loop()` `edge_is_mutual()` `edge_is_from()` `edge_is_to()` `edge_is_between()` `edge_is_incident()` `edge_is_bridge()` `edge_is_feedback_arc()`	Querying edge types
`edge_rank_eulerian()`	Calculate edge ranking
`random_walk_rank()`	Perform a random walk on the graph and return encounter rank
Graph measures The graph under investigation can also have properties of its own that can be summarised into quantitative or qualitative values.
`graph_is_simple()` `graph_is_directed()` `graph_is_bipartite()` `graph_is_connected()` `graph_is_tree()` `graph_is_forest()` `graph_is_dag()` `graph_is_chordal()` `graph_is_complete()` `graph_is_isomorphic_to()` `graph_is_subgraph_isomorphic_to()` `graph_is_eulerian()`	Querying graph types
`graph_adhesion()` `graph_assortativity()` `graph_automorphisms()` `graph_clique_num()` `graph_clique_count()` `graph_component_count()` `graph_motif_count()` `graph_diameter()` `graph_girth()` `graph_radius()` `graph_mutual_count()` `graph_asym_count()` `graph_unconn_count()` `graph_size()` `graph_order()` `graph_reciprocity()` `graph_min_cut()` `graph_mean_dist()` `graph_modularity()` `graph_efficiency()`	Graph measurements
Misc Outside of the verbs and algorithms there is a few helper functions available to streamline graph manipulation.
`.G()` `.N()` `.E()`	Access graph, nodes, and edges directly inside verbs
`with_graph()`	Evaluate a tidygraph algorithm in the context of a graph

Reference

New verbs

Graph creation

Mapping over nodes

Searches

Centrality

Community detection

Node measures

Edge measures

Graph measures

Misc