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() experimental: 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() experimental: 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

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() experimental: 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() experimental: 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() experimental: 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() experimental: Central vertices of a graph

is_biconnected() experimental: Check biconnectedness

realize_bipartite_degseq() experimental: Creating a bipartite graph from two degree sequences, deterministically

sample_chung_lu() chung_lu() experimental: Random graph with given expected degrees

voronoi_cells() experimental: Voronoi partitioning of a graph