Package index • igraph

igraph options
`igraph_options()` `igraph_opt()`	Parameters for the igraph package
`with_igraph_opt()`	Run code with a temporary igraph options setting
Construction
Deterministic constructors
`connect()` `ego_size()` `neighborhood_size()` `ego()` `neighborhood()` `make_ego_graph()` `make_neighborhood_graph()`	Neighborhood of graph vertices
`make_()`	Make a new graph
`make_bipartite_graph()` `bipartite_graph()`	Create a bipartite graph
`make_chordal_ring()` `chordal_ring()`	Create an extended chordal ring graph
`make_clusters()`	Creates a communities object.
`make_de_bruijn_graph()` `de_bruijn_graph()`	De Bruijn graphs
`make_empty_graph()` `empty_graph()`	A graph with no edges
`make_from_prufer()` `from_prufer()`	Create an undirected tree graph from its Prüfer sequence
`make_full_bipartite_graph()` `full_bipartite_graph()`	Create a full bipartite graph
`make_full_citation_graph()` `full_citation_graph()`	Create a complete (full) citation graph
`make_full_graph()` `full_graph()`	Create a full graph
`make_graph()` `make_directed_graph()` `make_undirected_graph()` `directed_graph()` `undirected_graph()`	Create an igraph graph from a list of edges, or a notable graph
`make_kautz_graph()` `kautz_graph()`	Kautz graphs
`make_lattice()` `lattice()`	Create a lattice graph
`make_line_graph()` `line_graph()`	Line graph of a graph
`make_ring()` `ring()`	Create a ring graph
`make_star()` `star()`	Create a star graph, a tree with n vertices and n - 1 leaves
`make_tree()` `tree()`	Create tree graphs
`realize_degseq()`	Creating a graph from a given degree sequence, deterministically
`realize_bipartite_degseq()`	Creating a bipartite graph from two degree sequences, deterministically
`graph_from_atlas()` `atlas()`	Create a graph from the Graph Atlas
`graph_from_edgelist()` `from_edgelist()`	Create a graph from an edge list matrix
`graph_from_literal()` `from_literal()`	Creating (small) graphs via a simple interface
`graph_()`	Convert object to a graph
`graph_from_lcf()`	Creating a graph from LCF notation
`as_data_frame()` `graph_from_data_frame()` `from_data_frame()`	Creating igraph graphs from data frames or vice-versa
Stochastic constructors (random graph models)
`sample_()`	Sample from a random graph model
`sample_bipartite()` `bipartite()`	Bipartite random graphs
`sample_chung_lu()` `chung_lu()`	Random graph with given expected degrees
`sample_correlated_gnp()`	Generate a new random graph from a given graph by randomly adding/removing edges
`sample_correlated_gnp_pair()`	Sample a pair of correlated $G(n,p)$ random graphs
`sample_degseq()` `degseq()`	Generate random graphs with a given degree sequence
`sample_dot_product()` `dot_product()`	Generate random graphs according to the random dot product graph model
`sample_fitness()`	Random graphs from vertex fitness scores
`sample_fitness_pl()`	Scale-free random graphs, from vertex fitness scores
`sample_forestfire()`	Forest Fire Network Model
`sample_gnm()` `gnm()`	Generate random graphs according to the $G(n,m)$ Erdős-Rényi model
`sample_gnp()` `gnp()`	Generate random graphs according to the $G(n,p)$ Erdős-Rényi model
`sample_grg()` `grg()`	Geometric random graphs
`sample_growing()` `growing()`	Growing random graph generation
`sample_hierarchical_sbm()` `hierarchical_sbm()`	Sample the hierarchical stochastic block model
`sample_islands()`	A graph with subgraphs that are each a random graph.
`sample_k_regular()`	Create a random regular graph
`sample_last_cit()` `last_cit()` `sample_cit_types()` `cit_types()` `sample_cit_cit_types()` `cit_cit_types()`	Random citation graphs
`sample_pa()` `pa()`	Generate random graphs using preferential attachment
`sample_pa_age()` `pa_age()`	Generate an evolving random graph with preferential attachment and aging
`sample_pref()` `pref()` `sample_asym_pref()` `asym_pref()`	Trait-based random generation
`sample_sbm()` `sbm()`	Sample stochastic block model
`sample_smallworld()` `smallworld()`	The Watts-Strogatz small-world model
`sample_traits_callaway()` `traits_callaway()` `sample_traits()` `traits()`	Graph generation based on different vertex types
`sample_tree()`	Sample trees randomly and uniformly
Constructor modifiers
`make_()`	Make a new graph
`sample_()`	Sample from a random graph model
`simplified()`	Constructor modifier to drop multiple and loop edges
`with_edge_()`	Constructor modifier to add edge attributes
`with_graph_()`	Constructor modifier to add graph attributes
`with_vertex_()`	Constructor modifier to add vertex attributes
`without_attr()`	Construtor modifier to remove all attributes from a graph
`without_loops()`	Constructor modifier to drop loop edges
`without_multiples()`	Constructor modifier to drop multiple edges
Convert to igraph
`as.igraph()`	Conversion to igraph
Adjacency matrices
`graph_from_adjacency_matrix()` `from_adjacency()`	Create graphs from adjacency matrices
Visualization
`add_layout_()`	Add layout to graph
`component_wise()`	Component-wise layout
`layout_()` `print(<igraph_layout_spec>)` `print(<igraph_layout_modifier>)`	Graph layouts
`layout_as_bipartite()` `as_bipartite()`	Simple two-row layout for bipartite graphs
`layout_as_star()` `as_star()`	Generate coordinates to place the vertices of a graph in a star-shape
`layout_as_tree()` `as_tree()`	The Reingold-Tilford graph layout algorithm
`layout_in_circle()` `in_circle()`	Graph layout with vertices on a circle.
`layout_nicely()` `nicely()`	Choose an appropriate graph layout algorithm automatically
`layout_on_grid()` `on_grid()`	Simple grid layout
`layout_on_sphere()` `on_sphere()`	Graph layout with vertices on the surface of a sphere
`layout_randomly()` `randomly()`	Randomly place vertices on a plane or in 3d space
`layout_with_dh()` `with_dh()`	The Davidson-Harel layout algorithm
`layout_with_fr()` `with_fr()`	The Fruchterman-Reingold layout algorithm
`layout_with_gem()` `with_gem()`	The GEM layout algorithm
`layout_with_graphopt()` `with_graphopt()`	The graphopt layout algorithm
`layout_with_kk()` `with_kk()`	The Kamada-Kawai layout algorithm
`layout_with_lgl()` `with_lgl()`	Large Graph Layout
`layout_with_mds()` `with_mds()`	Graph layout by multidimensional scaling
`layout_with_sugiyama()` `with_sugiyama()`	The Sugiyama graph layout generator
`merge_coords()` `layout_components()`	Merging graph layouts
`norm_coords()`	Normalize coordinates for plotting graphs
`normalize()`	Normalize layout
`layout_with_drl()` `with_drl()`	The DrL graph layout generator
`categorical_pal()`	Palette for categories
`diverging_pal()`	Diverging palette
`r_pal()`	The default R palette
`sequential_pal()`	Sequential palette
`plot(<igraph>)`	Plotting of graphs
`rglplot()`	3D plotting of graphs with OpenGL
`plot.common` `igraph.plotting`	Drawing graphs
`plot_dendrogram(<igraphHRG>)`	HRG dendrogram plot
`plot_dendrogram()`	Community structure dendrogram plots
`curve_multiple()`	Optimal edge curvature when plotting graphs
`shapes()` `shape_noclip()` `shape_noplot()` `add_shape()`	Various vertex shapes when plotting igraph graphs
`vertex.shape.pie`	Using pie charts as vertices in graph plots
Graph coloring
`greedy_vertex_coloring()`	Greedy vertex coloring
Functions for manipulating graphs
`add_edges()`	Add edges to a graph
`add_vertices()`	Add vertices to a graph
`complementer()`	Complementer of a graph
`compose()`	Compose two graphs as binary relations
`contract()`	Contract several vertices into a single one
`delete_edges()`	Delete edges from a graph
`delete_vertices()`	Delete vertices from a graph
`difference()`	Difference of two sets
`difference(<igraph>)`	Difference of graphs
`disjoint_union()` `%du%`	Disjoint union of graphs
`edge()` `edges()`	Helper function for adding and deleting edges
`connect()` `ego_size()` `neighborhood_size()` `ego()` `neighborhood()` `make_ego_graph()` `make_neighborhood_graph()`	Neighborhood of graph vertices
`-`(<igraph>)	Delete vertices or edges from a graph
`intersection()`	Intersection of two or more sets
`intersection(<igraph>)`	Intersection of graphs
`path()`	Helper function to add or delete edges along a path
`permute()`	Permute the vertices of a graph
`+`(<igraph>)	Add vertices, edges or another graph to a graph
`rep(<igraph>)` `*`(<igraph>)	Replicate a graph multiple times
`reverse_edges()` `t(<igraph>)`	Reverse edges in a graph
`simplify()` `is_simple()` `simplify_and_colorize()`	Simple graphs
`union()`	Union of two or more sets
`union(<igraph>)`	Union of graphs
`vertex()` `vertices()`	Helper function for adding and deleting vertices
Rewiring functions
`each_edge()`	Rewires the endpoints of the edges of a graph to a random vertex
`keeping_degseq()`	Graph rewiring while preserving the degree distribution
`rewire()`	Rewiring edges of a graph
Vertex, edge and graph attributes
`delete_edge_attr()`	Delete an edge attribute
`delete_graph_attr()`	Delete a graph attribute
`delete_vertex_attr()`	Delete a vertex attribute
`edge_attr<-`()	Set one or more edge attributes
`edge_attr()`	Query edge attributes of a graph
`edge_attr_names()`	List names of edge attributes
`graph_attr<-`()	Set all or some graph attributes
`graph_attr()`	Graph attributes of a graph
`graph_attr_names()`	List names of graph attributes
`igraph-attribute-combination` `attribute.combination`	How igraph functions handle attributes when the graph changes
`$`(<igraph>) `$<-`(<igraph>)	Getting and setting graph attributes, shortcut
`[[<-`(<igraph.vs>) `[<-`(<igraph.vs>) `$`(<igraph.vs>) `$<-`(<igraph.vs>) `V<-`()	Query or set attributes of the vertices in a vertex sequence
`set_edge_attr()`	Set edge attributes
`set_graph_attr()`	Set a graph attribute
`set_vertex_attr()`	Set vertex attributes
`vertex_attr<-`()	Set one or more vertex attributes
`vertex_attr()`	Query vertex attributes of a graph
`vertex_attr_names()`	List names of vertex attributes
Vertex and edge sequences
`E()`	Edges of a graph
`V()`	Vertices of a graph
`as_ids()`	Convert a vertex or edge sequence to an ordinary vector
`[[<-`(<igraph.es>) `[<-`(<igraph.es>) `$`(<igraph.es>) `$<-`(<igraph.es>) `E<-`()	Query or set attributes of the edges in an edge sequence
`[`(<igraph.es>)	Indexing edge sequences
`[[`(<igraph.es>)	Select edges and show their metadata
`[[<-`(<igraph.vs>) `[<-`(<igraph.vs>) `$`(<igraph.vs>) `$<-`(<igraph.vs>) `V<-`()	Query or set attributes of the vertices in a vertex sequence
`[`(<igraph.vs>)	Indexing vertex sequences
`[[`(<igraph.vs>)	Select vertices and show their metadata
`print(<igraph.es>)`	Print an edge sequence to the screen
`print(<igraph.vs>)`	Show a vertex sequence on the screen
`c(<igraph.es>)`	Concatenate edge sequences
`c(<igraph.vs>)`	Concatenate vertex sequences
`difference(<igraph.es>)`	Difference of edge sequences
`difference(<igraph.vs>)`	Difference of vertex sequences
`intersection(<igraph.es>)`	Intersection of edge sequences
`intersection(<igraph.vs>)`	Intersection of vertex sequences
`rev(<igraph.es>)`	Reverse the order in an edge sequence
`rev(<igraph.vs>)`	Reverse the order in a vertex sequence
`union(<igraph.es>)`	Union of edge sequences
`union(<igraph.vs>)`	Union of vertex sequences
`unique(<igraph.es>)`	Remove duplicate edges from an edge sequence
`unique(<igraph.vs>)`	Remove duplicate vertices from a vertex sequence
Utilities
Graph ID, comparison, name, weight
`graph_id()`	Get the id of a graph
`identical_graphs()`	Decide if two graphs are identical
`is_igraph()`	Is this object an igraph graph?
`is_named()`	Named graphs
`is_weighted()`	Weighted graphs
`is_chordal()`	Chordality of a graph
Conversion
`as.matrix(<igraph>)`	Convert igraph objects to adjacency or edge list matrices
`as_adj_list()` `as_adj_edge_list()`	Adjacency lists
`as_adjacency_matrix()`	Convert a graph to an adjacency matrix
`as_biadjacency_matrix()`	Bipartite adjacency matrix of a bipartite graph
`as_directed()` `as_undirected()`	Convert between directed and undirected graphs
`as_edgelist()`	Convert a graph to an edge list
`as_graphnel()`	Convert igraph graphs to graphNEL objects from the graph package
`as_long_data_frame()`	Convert a graph to a long data frame
`graph_from_adj_list()`	Create graphs from adjacency lists
`as_data_frame()` `graph_from_data_frame()` `from_data_frame()`	Creating igraph graphs from data frames or vice-versa
`graph_from_graphnel()`	Convert graphNEL objects from the graph package to igraph
Env and data
`dot-data` `.data` `dot-env` `.env`	`.data` and `.env` pronouns
Printing
`head_print()`	Print the only the head of an R object
`indent_print()`	Indent a printout
`print(<igraph>)` `summary(<igraph>)`	Print graphs to the terminal
`is_printer_callback()`	Is this a printer callback?
`printer_callback()`	Create a printer callback function
Latent position vector samplers
`sample_dirichlet()`	Sample from a Dirichlet distribution
`sample_sphere_surface()`	Sample vectors uniformly from the surface of a sphere
`sample_sphere_volume()`	Sample vectors uniformly from the volume of a sphere
Miscellaneous
`convex_hull()`	Convex hull of a set of vertices
`running_mean()`	Running mean of a time series
`sample_seq()`	Sampling a random integer sequence
`fit_power_law()`	Fitting a power-law distribution function to discrete data
Structural properties
`bfs()`	Breadth-first search
`component_distribution()` `largest_component()` `components()` `is_connected()` `count_components()`	Connected components of a graph
`constraint()`	Burt's constraint
`coreness()`	K-core decomposition of graphs
`degree()` `max_degree()` `degree_distribution()`	Degree and degree distribution of the vertices
`dfs()`	Depth-first search
`distance_table()` `mean_distance()` `distances()` `shortest_paths()` `all_shortest_paths()`	Shortest (directed or undirected) paths between vertices
`edge_density()`	Graph density
`connect()` `ego_size()` `neighborhood_size()` `ego()` `neighborhood()` `make_ego_graph()` `make_neighborhood_graph()`	Neighborhood of graph vertices
`feedback_arc_set()`	Finding a feedback arc set in a graph
`girth()`	Girth of a graph
`is_acyclic()`	Acyclic graphs
`is_dag()`	Directed acyclic graphs
`k_shortest_paths()`	Find the $k$ shortest paths between two vertices
`knn()`	Average nearest neighbor degree
`is_matching()` `is_max_matching()` `max_bipartite_match()`	Matching
`reciprocity()`	Reciprocity of graphs
`subcomponent()`	In- or out- component of a vertex
`subgraph()` `induced_subgraph()` `subgraph_from_edges()`	Subgraph of a graph
`topo_sort()`	Topological sorting of vertices in a graph
`transitivity()`	Transitivity of a graph
`unfold_tree()`	Convert a general graph into a forest
`which_multiple()` `any_multiple()` `count_multiple()` `which_loop()` `any_loop()`	Find the multiple or loop edges in a graph
`which_mutual()`	Find mutual edges in a directed graph
`cocitation()` `bibcoupling()`	Cocitation coupling
`similarity()`	Similarity measures of two vertices
`cohesive_blocks()` `length(<cohesiveBlocks>)` `blocks()` `graphs_from_cohesive_blocks()` `cohesion(<cohesiveBlocks>)` `hierarchy()` `parent()` `print(<cohesiveBlocks>)` `summary(<cohesiveBlocks>)` `plot(<cohesiveBlocks>)` `plot_hierarchy()` `export_pajek()` `max_cohesion()`	Calculate Cohesive Blocks
`triangles()` `count_triangles()`	Find triangles in graphs
`assortativity()` `assortativity_nominal()` `assortativity_degree()`	Assortativity coefficient
`spectrum()`	Eigenvalues and eigenvectors of the adjacency matrix of a graph
Matrices
`laplacian_matrix()`	Graph Laplacian
`as_adjacency_matrix()`	Convert a graph to an adjacency matrix
`stochastic_matrix()`	Stochastic matrix of a graph
Chordal graphs
`is_chordal()`	Chordality of a graph
`max_cardinality()`	Maximum cardinality search
Triangles and transitivity
`triangles()` `count_triangles()`	Find triangles in graphs
`transitivity()`	Transitivity of a graph
Paths
`all_simple_paths()`	List all simple paths from one source
`diameter()` `get_diameter()` `farthest_vertices()`	Diameter of a graph
`distance_table()` `mean_distance()` `distances()` `shortest_paths()` `all_shortest_paths()`	Shortest (directed or undirected) paths between vertices
`eccentricity()`	Eccentricity of the vertices in a graph
`graph_center()`	Central vertices of a graph
`radius()`	Radius of a graph
Bipartite graphs
`bipartite_mapping()`	Decide whether a graph is bipartite
`bipartite_projection()` `bipartite_projection_size()`	Project a bipartite graph
`is_bipartite()`	Checks whether the graph has a vertex attribute called `type`.
`make_bipartite_graph()` `bipartite_graph()`	Create a bipartite graph
`graph_from_biadjacency_matrix()`	Create graphs from a bipartite adjacency matrix
`as_data_frame()` `graph_from_data_frame()` `from_data_frame()`	Creating igraph graphs from data frames or vice-versa
Efficiency
`global_efficiency()` `local_efficiency()` `average_local_efficiency()`	Efficiency of a graph
Similarity
`similarity()`	Similarity measures of two vertices
Trees
`is_forest()`	Decide whether a graph is a forest.
`is_tree()`	Decide whether a graph is a tree.
`make_from_prufer()` `from_prufer()`	Create an undirected tree graph from its Prüfer sequence
`sample_spanning_tree()`	Samples from the spanning trees of a graph randomly and uniformly
`to_prufer()`	Convert a tree graph to its Prüfer sequence
`mst()`	Minimum spanning tree
Structural queries
`adjacent_vertices()`	Adjacent vertices of multiple vertices in a graph
`are_adjacent()`	Are two vertices adjacent?
`ends()`	Incident vertices of some graph edges
`get_edge_ids()`	Find the edge ids based on the incident vertices of the edges
`vcount()` `gorder()`	Order (number of vertices) of a graph
`gsize()` `ecount()`	The size of the graph (number of edges)
`head_of()`	Head of the edge(s) in a graph
`incident()`	Incident edges of a vertex in a graph
`incident_edges()`	Incident edges of multiple vertices in a graph
`is_directed()`	Check whether a graph is directed
`neighbors()`	Neighboring (adjacent) vertices in a graph
`[`(<igraph>)	Query and manipulate a graph as it were an adjacency matrix
`[[`(<igraph>)	Query and manipulate a graph as it were an adjacency list
`tail_of()`	Tails of the edge(s) in a graph
ARPACK eigenvector calculation
`arpack_defaults()` `arpack()`	ARPACK eigenvector calculation
Centrality measures
`alpha_centrality()`	Find Bonacich alpha centrality scores of network positions
`betweenness()` `edge_betweenness()`	Vertex and edge betweenness centrality
`closeness()`	Closeness centrality of vertices
`diversity()`	Graph diversity
`eigen_centrality()`	Eigenvector centrality of vertices
`harmonic_centrality()`	Harmonic centrality of vertices
`hits_scores()`	Kleinberg's hub and authority centrality scores.
`authority_score()` `hub_score()`	Kleinberg's authority centrality scores.
`page_rank()`	The Page Rank algorithm
`power_centrality()`	Find Bonacich Power Centrality Scores of Network Positions
`spectrum()`	Eigenvalues and eigenvectors of the adjacency matrix of a graph
`strength()`	Strength or weighted vertex degree
`subgraph_centrality()`	Find subgraph centrality scores of network positions
Centralization
`centr_betw()`	Centralize a graph according to the betweenness of vertices
`centr_betw_tmax()`	Theoretical maximum for betweenness centralization
`centr_clo()`	Centralize a graph according to the closeness of vertices
`centr_clo_tmax()`	Theoretical maximum for closeness centralization
`centr_degree()`	Centralize a graph according to the degrees of vertices
`centr_degree_tmax()`	Theoretical maximum for degree centralization
`centr_eigen()`	Centralize a graph according to the eigenvector centrality of vertices
`centr_eigen_tmax()`	Theoretical maximum for eigenvector centralization
`centralize()`	Centralization of a graph
Scan statistics
`local_scan()`	Compute local scan statistics on graphs
`scan_stat()`	Scan statistics on a time series of graphs
Graph motifs and subgraphs
`count_motifs()`	Graph motifs
`dyad_census()`	Dyad census of a graph
`motifs()`	Graph motifs
`sample_motifs()`	Graph motifs
`triad_census()`	Triad census, subgraphs with three vertices
Graph isomorphism
`canonical_permutation()`	Canonical permutation of a graph
`count_isomorphisms()`	Count the number of isomorphic mappings between two graphs
`count_subgraph_isomorphisms()`	Count the isomorphic mappings between a graph and the subgraphs of another graph
`graph_from_isomorphism_class()`	Create a graph from an isomorphism class
`isomorphic()` `is_isomorphic_to()`	Decide if two graphs are isomorphic
`isomorphism_class()`	Isomorphism class of a graph
`isomorphisms()`	Calculate all isomorphic mappings between the vertices of two graphs
`subgraph_isomorphic()` `is_subgraph_isomorphic_to()`	Decide if a graph is subgraph isomorphic to another one
`subgraph_isomorphisms()`	All isomorphic mappings between a graph and subgraphs of another graph
`simplify()` `is_simple()` `simplify_and_colorize()`	Simple graphs
`automorphism_group()`	Generating set of the automorphism group of a graph
`count_automorphisms()`	Number of automorphisms
`permute()`	Permute the vertices of a graph
Graph matching
`match_vertices()`	Match Graphs given a seeding of vertex correspondences
Maximum flow and connectivity
`dominator_tree()`	Dominator tree
`edge_connectivity()` `edge_disjoint_paths()` `adhesion()`	Edge connectivity
`is_min_separator()`	Minimal vertex separators
`is_separator()`	Check whether removing this set of vertices would disconnect the graph.
`max_flow()`	Maximum flow in a graph
`min_cut()`	Minimum cut in a graph
`min_separators()`	Minimum size vertex separators
`min_st_separators()`	Minimum size vertex separators
`st_cuts()`	List all (s,t)-cuts of a graph
`st_min_cuts()`	List all minimum $(s,t)$-cuts of a graph
`vertex_connectivity()` `vertex_disjoint_paths()` `cohesion(<igraph>)`	Vertex connectivity
Cliques
`cliques()` `largest_cliques()` `max_cliques()` `count_max_cliques()` `clique_num()` `largest_weighted_cliques()` `weighted_clique_num()` `clique_size_counts()`	Functions to find cliques, i.e. complete subgraphs in a graph
`ivs()` `largest_ivs()` `max_ivs()` `ivs_size()` `independence_number()`	Independent vertex sets
`weighted_cliques()`	Functions to find weighted cliques, i.e. vertex-weighted complete subgraphs in a graph
`graphlet_basis()` `graphlet_proj()` `graphlets()`	Graphlet decomposition of a graph
Community detection
`as_membership()`	Declare a numeric vector as a membership vector
`cluster_edge_betweenness()`	Community structure detection based on edge betweenness
`cluster_fast_greedy()`	Community structure via greedy optimization of modularity
`cluster_fluid_communities()`	Community detection algorithm based on interacting fluids
`cluster_infomap()`	Infomap community finding
`cluster_label_prop()`	Finding communities based on propagating labels
`cluster_leading_eigen()`	Community structure detecting based on the leading eigenvector of the community matrix
`cluster_leiden()`	Finding community structure of a graph using the Leiden algorithm of Traag, van Eck & Waltman.
`cluster_louvain()`	Finding community structure by multi-level optimization of modularity
`cluster_optimal()`	Optimal community structure
`cluster_spinglass()`	Finding communities in graphs based on statistical meachanics
`cluster_walktrap()`	Community structure via short random walks
`membership()` `print(<communities>)` `modularity(<communities>)` `length(<communities>)` `sizes()` `algorithm()` `merges()` `crossing()` `code_len()` `is_hierarchical()` `as.dendrogram(<communities>)` `as.hclust(<communities>)` `cut_at()` `show_trace()` `plot(<communities>)` `communities()`	Functions to deal with the result of network community detection
`compare()`	Compares community structures using various metrics
`groups()`	Groups of a vertex partitioning
`make_clusters()`	Creates a communities object.
`modularity(<igraph>)` `modularity_matrix()`	Modularity of a community structure of a graph
`plot_dendrogram()`	Community structure dendrogram plots
`split_join_distance()`	Split-join distance of two community structures
`voronoi_cells()`	Voronoi partitioning of a graph
Graph cycles
`feedback_arc_set()`	Finding a feedback arc set in a graph
`girth()`	Girth of a graph
`has_eulerian_path()` `has_eulerian_cycle()` `eulerian_path()` `eulerian_cycle()`	Find Eulerian paths or cycles in a graph
`is_acyclic()`	Acyclic graphs
`is_dag()`	Directed acyclic graphs
Connected components
`articulation_points()` `bridges()`	Articulation points and bridges of a graph
`biconnected_components()`	Biconnected components
`component_distribution()` `largest_component()` `components()` `is_connected()` `count_components()`	Connected components of a graph
`decompose()`	Decompose a graph into components
`is_biconnected()`	Check biconnectedness
Spectral embedding
`dim_select()`	Dimensionality selection for singular values using profile likelihood.
`embed_adjacency_matrix()`	Spectral Embedding of Adjacency Matrices
`embed_laplacian_matrix()`	Spectral Embedding of the Laplacian of a Graph
Hierarchical random graphs
`consensus_tree()`	Create a consensus tree from several hierarchical random graph models
`fit_hrg()`	Fit a hierarchical random graph model
`hrg-methods`	Hierarchical random graphs
`hrg()`	Create a hierarchical random graph from an igraph graph
`hrg_tree()`	Create an igraph graph from a hierarchical random graph model
`predict_edges()`	Predict edges based on a hierarchical random graph model
`print(<igraphHRG>)`	Print a hierarchical random graph model to the screen
`print(<igraphHRGConsensus>)`	Print a hierarchical random graph consensus tree to the screen
`sample_hrg()`	Sample from a hierarchical random graph model
Graphical degree sequences
`is_degseq()`	Check if a degree sequence is valid for a multi-graph
`is_graphical()`	Is a degree sequence graphical?
Processes on graphs
`plot(<sir>)`	Plotting the results on multiple SIR model runs
`time_bins()` `median(<sir>)` `quantile(<sir>)` `sir()`	SIR model on graphs
`random_walk()` `random_edge_walk()`	Random walk on a graph
Demo
`igraph_demo()`	Run igraph demos, step by step
I/O read/write files
`graph_from_graphdb()`	Load a graph from the graph database for testing graph isomorphism.
`read_graph()`	Reading foreign file formats
`write_graph()`	Writing the graph to a file in some format
Interactive functions
`tkplot()` `tk_close()` `tk_off()` `tk_fit()` `tk_center()` `tk_reshape()` `tk_postscript()` `tk_coords()` `tk_set_coords()` `tk_rotate()` `tk_canvas()`	Interactive plotting of graphs
`console()`	The igraph console
Versions
`graph_version()`	igraph data structure versions
`upgrade_graph()`	igraph data structure versions
Experimental functions
`graph_center()`	Central vertices of a graph
`is_biconnected()`	Check biconnectedness
`realize_bipartite_degseq()`	Creating a bipartite graph from two degree sequences, deterministically
`sample_chung_lu()` `chung_lu()`	Random graph with given expected degrees
`voronoi_cells()`	Voronoi partitioning of a graph

Reference

igraph options

Construction

Deterministic constructors

Stochastic constructors (random graph models)

Constructor modifiers

Convert to igraph

Adjacency matrices

Visualization

Graph coloring

Functions for manipulating graphs

Rewiring functions

Vertex, edge and graph attributes

Vertex and edge sequences

Utilities

Graph ID, comparison, name, weight

Conversion

Env and data

Printing

Latent position vector samplers

Miscellaneous

Structural properties

Matrices

Chordal graphs

Triangles and transitivity

Paths

Bipartite graphs

Efficiency

Similarity

Trees

Structural queries

ARPACK eigenvector calculation

Centrality measures

Centralization

Scan statistics

Graph motifs and subgraphs

Graph isomorphism

Graph matching

Maximum flow and connectivity

Cliques

Community detection

Graph cycles

Connected components

Spectral embedding

Hierarchical random graphs

Graphical degree sequences

Processes on graphs

Demo

I/O read/write files

Interactive functions

Versions

Experimental functions