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() deprecated: Bipartite random graphs
bipartite_gnm() bipartite_gnp() sample_bipartite_gnm() sample_bipartite_gnp(): 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
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
align_layout(): Align a vertex layout This function centers a vertex layout on the coordinate system origin and rotates the layout to achieve a visually pleasing alignment with the coordinate axes. Doing this is particularly useful with force-directed layouts such as layout_with_fr().

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
set_vertex_attrs(): Set multiple 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
feedback_vertex_set() experimental: Finding a feedback vertex 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() is_clique(): Functions to find cliques, i.e. complete subgraphs in a graph
is_complete(): Is this a complete graph?
ivs() largest_ivs() max_ivs() ivs_size() independence_number() is_ivs(): 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
feedback_vertex_set() experimental: Finding a feedback vertex set in a graph
find_cycle() experimental: Finds a cycle in a graph, if there is one
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
simple_cycles() experimental: Finds all simple cycles in a graph.

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

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

feedback_vertex_set() experimental: Finding a feedback vertex set in a graph
find_cycle() experimental: Finds a cycle in a graph, if there is one
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
simple_cycles() experimental: Finds all simple cycles in a graph.
voronoi_cells() experimental: Voronoi partitioning of a graph