- Installation
- Usage
- Creating a graph
- Vertex and edge IDs
- Adding/deleting vertices and edges
- Constructing graphs
- Setting and retrieving attributes
- Structural properties of graphs
- Querying vertices and edges based on attributes
- Treating a graph as an adjacency matrix
- Layouts and plotting
- igraph and the outside world
- Where to go next
- Session info

`igraph`

is a fast and open source library for the
analysis of graphs or networks. The library consists of a core written
in C and bindings for high-level languages including R, Python, and Mathematica.
This vignette aims to give you an overview of the functions available in
the R interface of `igraph`

. For detailed function by
function API documentation, check out https://r.igraph.org/reference/.

**NOTE:** Throughout this tutorial, we will use words
`graph`

and `network`

as synonyms, and also
`vertex`

or `node`

as synonyms.

To install the library from CRAN, use:

More details on dependencies, requirements, and troubleshooting on installation are found on the main documentation page.

To use `igraph`

in your R code, you must first load the
library:

```
##
## Attaching package: 'igraph'
```

```
## The following objects are masked from 'package:stats':
##
## decompose, spectrum
```

```
## The following object is masked from 'package:base':
##
## union
```

Now you have all `igraph`

functions available.

`igraph`

offers many ways to create a graph. The simplest
one is the function `make_empty_graph()`

:

The most common way to create a graph is `make_graph()`

,
which constructs a network based on specified edges. For example, to
make a graph with 10 nodes (numbered `1`

to `10`

)
and two edges connecting nodes `1-2`

and
`1-5`

:

Starting from igraph 0.8.0, you can also include literal here, via
igraph’s formula notation. In this case, the first term of the formula
has to start with a `~`

character, just like regular formulae
in R. The expressions consist of vertex names and edge operators. An
edge operator is a sequence of `-`

and `+`

characters, the former is for the edges and the latter is used for arrow
heads. The edges can be arbitrarily long, that is to say, you may use as
many `-`

characters to “draw” them as you like. If all edge
operators consist of only `-`

characters then the graph will
be undirected, whereas a single `+`

character implies a
directed graph: that is to say to create the same graph as above:

We can print the graph to get a summary of its nodes and edges:

```
## IGRAPH 5546ea9 UN-- 10 2 --
## + attr: name (v/c)
## + edges from 5546ea9 (vertex names):
## [1] 1--2 1--5
```

This means: **U**ndirected **N**amed graph
with **10** vertices and **2** edges, with the
exact edges listed out. If the graph has a `[name]`

attribute, it is printed as well.

**NOTE**: `summary()`

does not list the
edges, which is convenient for large graphs with millions of edges:

```
## IGRAPH 5546ea9 UN-- 10 2 --
## + attr: name (v/c)
```

The same function `make_graph()`

can create some notable
graphs by just specifying their name. For example you can create the
graph that represents the social network of Zachary’s karate club, that
shows the friendship between 34 members of a karate club at a US
university in the 1970s:

To visualize a graph you can use `plot()`

:

A more detailed description of plotting options is provided later on in this tutorial.

Vertices and edges have numerical vertex IDs in igraph. Vertex IDs
are always consecutive and they start with 1. For a graph with n
vertices the vertex IDs are always between 1 and n. If some operation
changes the number of vertices in the graphs, for instance a subgraph is
created via `induced_subgraph()`

, then the vertices are
renumbered to satisfy this criterion.

The same is true for the edges as well: edge IDs are always between 1 and m, the total number of edges in the graph.

**NOTE**: If you are familiar with the C core or the Python interface of
`igraph`

, you might have noticed that in those languages
vertex and edge IDs start from 0. In the R interface, both start from 1
instead, to keep consistent with the convention in each language.

In addition to IDs, vertices and edges can be assigned a name and other attributes. That makes it easier to track them whenever the graph is altered. Examples of this pattern are shown later on in this tutorial.

Let’s continue working with the Karate club graph. To add one or more
vertices to an existing graph, use `add_vertices()`

:

Similarly, to add edges you can use `add_edges()`

:

Edges are added by specifying the source and target vertex IDs for
each edge. This call added three edges, one connecting vertices
`1`

and `35`

, one connecting vertices
`1`

and `36`

, and one connecting vertices
`34`

and `37`

.

In addition to the `add_vertices()`

and
`add_edges()`

functions, the plus operator can be used to add
vertices or edges to graph. The actual operation that is performed
depends on the type of the right hand side argument:

You can add a single vertex/edge at a time using
`add_vertex()`

and `add_edge()`

(singular).

**Warning**: If you need to add multiple edges to a
graph, it is much more efficient to call `add_edges()`

once
rather than repeatedly calling `add_edge()`

with a single new
edge. The same applies when deleting edges and vertices.

If you try to add edges to vertices with invalid IDs (i.e., you try
to add an edge to vertex `38`

when the graph has only 37
vertices), `igraph`

shows an error:

`## Error in add_edges(g, edges = c(38, 37)): At vendor/cigraph/src/graph/type_indexededgelist.c:261 : Out-of-range vertex IDs when adding edges. Invalid vertex ID`

Let us add some more vertices and edges to our graph. In
`igraph`

we can use the `magrittr`

package, which
provides a mechanism for chaining commands with the operator
`%\>%`

:

```
g <- g %>%
add_edges(edges = c(1, 34)) %>%
add_vertices(3) %>%
add_edges(edges = c(38, 39, 39, 40, 40, 38, 40, 37))
g
```

```
## IGRAPH 9d8b3c7 U--- 40 86 -- Zachary
## + attr: name (g/c)
## + edges from 9d8b3c7:
## [1] 1-- 2 1-- 3 1-- 4 1-- 5 1-- 6 1-- 7 1-- 8 1-- 9 1--11 1--12
## [11] 1--13 1--14 1--18 1--20 1--22 1--32 2-- 3 2-- 4 2-- 8 2--14
## [21] 2--18 2--20 2--22 2--31 3-- 4 3-- 8 3--28 3--29 3--33 3--10
## [31] 3-- 9 3--14 4-- 8 4--13 4--14 5-- 7 5--11 6-- 7 6--11 6--17
## [41] 7--17 9--31 9--33 9--34 10--34 14--34 15--33 15--34 16--33 16--34
## [51] 19--33 19--34 20--34 21--33 21--34 23--33 23--34 24--26 24--28 24--33
## [61] 24--34 24--30 25--26 25--28 25--32 26--32 27--30 27--34 28--34 29--32
## [71] 29--34 30--33 30--34 31--33 31--34 32--33 32--34 33--34 1--35 1--36
## + ... omitted several edges
```

We now have an undirected graph with 40 vertices and 86 edges. Vertex
and edge IDs are always *contiguous*, so if you delete a vertex
all subsequent vertices will be renumbered. When a vertex is renumbered,
edges are **not** renumbered, but their source and target
vertices will be. Use `delete_vertices()`

and
`delete_edges()`

to perform these operations. For instance,
to delete the edge connecting vertices `1-34`

, get its ID and
then delete it:

`## [1] 82`

As an example, to create a broken ring:

The example above shows that you can also refer to edges with strings
containing the IDs of the source and target vertices, connected by a
pipe symbol `|`

. `"10|1"`

in the above example
means the edge that connects vertex 10 to vertex 1. Of course you can
also use the edge IDs directly, or retrieve them with the
`get.edge.ids()`

function:

As another example, let’s make a chordal graph. Remember that a graph
is chordal (or triangulated) if each of its cycles of four or more nodes
has a chord, which is an edge joining two nodes that are not adjacent in
the cycle. First, let’s create the initial graph using
`graph_from_literal()`

:

```
g1 <- graph_from_literal(
A - B:C:I, B - A:C:D,
C - A:B:E:H,
D - B:E:F,
E - C:D:F:H,
F - D:E:G,
G - F:H,
H - C:E:G:I,
I - A:H
)
plot(g1)
```

In the example above, the `:`

operator was used to define
vertex sets. If an edge operator connects two vertex sets, then every
vertex from the first set will be connected to every vertex in the
second set. Then we use `is_chordal()`

to evaluate if our
graph is chordal and to search what edges are missing to fill-in the
graph:

```
## $chordal
## [1] FALSE
##
## $fillin
## [1] 2 6 8 7 5 7 2 7 6 1 7 1
##
## $newgraph
## NULL
```

We can then add the edges required to make the initial graph chordal in a single line:

In addition to `make_empty_graph()`

,
`make_graph()`

, and `make_graph_from_literal()`

,
`igraph`

includes many other function to construct a graph.
Some are *deterministic*, that is to say they produce the same
graph each single time, for instance `make_tree()`

:

```
## IGRAPH 44a2acb U--- 5 3 -- Ring graph
## + attr: name (g/c), mutual (g/l), circular (g/l)
```

This generates a regular tree graph with 127 vertices, each vertex
having two children. No matter how many times you call
`make_tree()`

, the generated graph will always be the same if
you use the same parameters:

`## [1] TRUE`

Other functions generate graphs *stochastically*, which means
they produce a different graph each time. For instance
`sample_grg()`

:

```
## IGRAPH d29faea U--- 100 507 -- Geometric random graph
## + attr: name (g/c), radius (g/n), torus (g/l)
```

This generates a geometric random graph: *n* points are chosen
randomly and uniformly inside the unit square and pairs of points closer
to each other than a predefined distance *d* are connected by an
edge. If you generate GRGs with the same parameters, they will be
different:

`## [1] FALSE`

A slightly looser way to check if the graphs are equivalent is via
`isomorphic`

. Two graphs are said to be isomorphic if they
have the same number of components (vertices and edges) and maintain a
one-to-one correspondence between vertices and edges, that is to say,
they are connected in the same way.

`## [1] FALSE`

Checking for isomorphism can take a while for large graphs (in this
case, the answer can quickly be given by checking the degree sequence of
the two graphs). `identical_graph()`

is a stricter criterion
than `isomorphic()`

: the two graphs must have the same list
of vertices and edges, in exactly the same order, with same
directedness, and the two graphs must also have identical graph, vertex
and edge attributes.

In addition to IDs, vertex and edges can have *attributes*
such as a name, coordinates for plotting, metadata, and weights. The
graph itself can have such attributes too (for instance a name, which
will show in `summary()`

). In a sense, every graph, vertex
and edge can be used as an R namespace to store and retrieve these
attributes.

To demonstrate the use of attributes, let us create a simple social network:

```
g <- make_graph(
~ Alice - Boris:Himari:Moshe, Himari - Alice:Nang:Moshe:Samira,
Ibrahim - Nang:Moshe, Nang - Samira
)
```

Each vertex represents a person, so we want to store ages, genders
and types of connection between two people (`is_formal()`

refers to whether a connection between one person or another is formal
or informal, respectively colleagues or friends). The `\$`

operator is a shortcut to get and set graph attributes. It is shorter
and just as readable as `graph_attr()`

and
`set_graph_attr()`

.

```
V(g)$age <- c(25, 31, 18, 23, 47, 22, 50)
V(g)$gender <- c("f", "m", "f", "m", "m", "f", "m")
E(g)$is_formal <- c(FALSE, FALSE, TRUE, TRUE, TRUE, FALSE, TRUE, FALSE, FALSE)
summary(g)
```

```
## IGRAPH fddc1b6 UN-- 7 9 --
## + attr: name (v/c), age (v/n), gender (v/c), is_formal (e/l)
```

`V()`

and `E()`

are the standard way to obtain
a sequence of all vertices and edges, respectively. This assigns an
attribute to *all* vertices/edges at once. Another way to
generate our social network is with the use of
`set_vertex_attr()`

and `set_edge_attr()`

and the
operator `%\>%`

:

```
g <- make_graph(
~ Alice - Boris:Himari:Moshe, Himari - Alice:Nang:Moshe:Samira,
Ibrahim - Nang:Moshe, Nang - Samira
) %>%
set_vertex_attr("age", value = c(25, 31, 18, 23, 47, 22, 50)) %>%
set_vertex_attr("gender", value = c("f", "m", "f", "m", "m", "f", "m")) %>%
set_edge_attr("is_formal", value = c(FALSE, FALSE, TRUE, TRUE, TRUE, FALSE, TRUE, FALSE, FALSE))
summary(g)
```

To assign or modify an attribute for a single vertex/edge:

`## [1] FALSE FALSE TRUE TRUE TRUE FALSE TRUE FALSE FALSE`

`## [1] TRUE FALSE TRUE TRUE TRUE FALSE TRUE FALSE FALSE`

Attribute values can be set to any R object, but note that storing the graph in some file formats might result in the loss of complex attribute values. Vertices, edges and the graph itself can all be used to set attributes, for instance to add a date to the graph:

`## [1] "2022-02-11"`

To retrieve attributes, you can also use `graph_attr()`

,
`vertex_attr()`

, and `edge_attr()`

. To find the ID
of a vertex you can use the function `match()`

:

`## [1] 7`

To assign attributes to a subset of vertices or edges, you can use:

```
## + 7/7 vertices, named, from fddc1b6:
## [1] Alejandra Bruno Carmina Moshe Nang Samira Ibrahim
```

To delete attributes:

`## NULL`

If you want to save a graph in R with all the attributes use the R’s
standard function `dput()`

function and retrieve it later
with `dget()`

. You can also just save the R workspace and
restore it later.

`igraph`

provides a large set of functions to calculate
various structural properties of graphs. It is beyond the scope of this
tutorial to document all of them, hence this section will only introduce
a few of them for illustrative purposes. We will work on the small
social network constructed in the previous section.

Perhaps the simplest property one can think of is the
*degree*. The degree of a vertex equals the number of edges
adjacent to it. In case of directed networks, we can also define
*in-degree* (the number of edges pointing towards the vertex) and
*out-degree* (the number of edges originating from the vertex).
`igraph`

is able to calculate all of them using a simple
syntax:

```
## Alejandra Bruno Carmina Moshe Nang Samira Ibrahim
## 3 1 4 3 3 2 2
```

If the graph was directed, we would have been able to calculate the
in- and out-degrees separately using `degree(mode = "in")`

and `degree(mode = "out")`

. You can also pass a single vertex
ID or a list of vertex IDs to `degree()`

if you want to
calculate the degrees for only a subset of vertices:

```
## Ibrahim
## 2
```

```
## Carmina Moshe Nang
## 4 3 3
```

Most functions that accept vertex IDs also accept vertex
*names* (the values of the `name`

vertex attribute) as
long as the names are unique:

```
## Carmina Moshe Nang
## 4 3 3
```

It also works for single vertices:

```
## Bruno
## 1
```

A similar syntax is used for most of the structural properties
`igraph`

can calculate. For vertex properties, the functions
accept a vertex ID, a vertex name, or a list of vertex IDs or names (and
if they are omitted, the default is the set of all vertices). For edge
properties, the functions accept a single edge ID or a list of edge
IDs.

**NOTE:** For some measures, it does not make sense to
calculate them only for a few vertices or edges instead of the whole
graph, as it would take the same time anyway. In this case, the
functions won’t accept vertex or edge IDs, but you can still restrict
the resulting list later using standard operations. One such example is
eigenvector centrality (`evcent()`

).

Besides degree, igraph includes built-in routines to calculate many
other centrality properties, including vertex and edge betweenness
(`edge_betweenness()`

) or Google’s PageRank
(`page_rank()`

) just to name a few. Here we just illustrate
edge betweenness:

`## [1] 6 6 4 3 4 4 4 2 3`

Now we can also figure out which connections have the highest betweenness centrality:

```
## [,1] [,2]
## [1,] "Alejandra" "Bruno"
## [2,] "Alejandra" "Carmina"
```

Imagine that in a given social network, you want to find out who has
the largest degree. You can do that with the tools presented so far and
the `which.max()`

function:

```
## Carmina
## 3
```

Another example would be to select only vertices that have only odd
IDs but not even ones, using the `V()`

function:

```
graph <- graph.full(n = 10)
only_odd_vertices <- which(V(graph) %% 2 == 1)
length(only_odd_vertices)
```

`## [1] 5`

Of course, it is possible to select vertices or edges by positional indices:

```
## + 3/10 vertices, from f1741e6:
## [1] 2 3 7
```

```
## + 2/10 vertices, from f1741e6:
## [1] 2 7
```

Selecting a vertex that does not exist results in an error:

```
seq <- V(graph)[2, 3, 7, "foo", 3.5]
## Error in simple_vs_index(x, ii, na_ok) : Unknown vertex selected
```

Attribute names can also be used as-is within the indexing brackets
of `V()`

and `E()`

. This can be combined with R’s
ability to use Boolean vectors for indexing to obtain very concise and
readable expressions to retrieve a subset of the vertex or edge set of a
graph. For instance, the following command gives you the names of the
individuals younger than 30 years in our social network:

`## [1] "Alejandra" "Carmina" "Moshe" "Samira"`

Of course, `<`

is not the only boolean operator that
can be used for this. Other possibilities include the following:

Operator | Meaning |
---|---|

`==` |
The attribute/property value must be equal to |

`!=` |
The attribute/property value must not be equal to |

`<` |
The attribute/property value must be less than |

`<=` |
The attribute/property value must be less than or equal
to |

`>` |
The attribute/property value must be greater than |

`>=` |
The attribute/property value must be greater than or equal
to |

`%in%` |
The attribute/property value must be included in |

You can also create a “not in” operator from `%in%`

using
the `Negate()`

function:

If an attribute has the same name as an `igraph`

function,
you should be careful as the syntax can become a little confusing. For
instance, if there is an attribute named `degree`

that
represents the grades of an exam for each person, that should not be
confused with the `igraph`

function that computes the degrees
of vertices in a network sense:

`## [1] "A" "A+" "C"`

`## [1] "Alejandra" "Moshe" "Nang"`

Edges can be selected based on attributes just like vertices. As
mentioned above, the standard way to get edges is `E`

.
Moreover, there are a few special structural properties for selecting
edges.

Using `.from()`

allows you to filter the edge sequence
based on the source vertices of the edges. For instance, to select all
the edges originating from Carmina (who has vertex index 3):

```
## + 4/9 edges from fddc1b6 (vertex names):
## [1] Alejandra--Carmina Carmina --Moshe Carmina --Nang Carmina --Samira
```

Of course it also works with vertex names:

```
## + 4/9 edges from fddc1b6 (vertex names):
## [1] Alejandra--Carmina Carmina --Moshe Carmina --Nang Carmina --Samira
```

Using `.to()`

filters edge sequences based on the target
vertices. This is different from `.from()`

if the graph is
directed, while it gives the same answer for undirected graphs. Using
`.inc()`

selects only those edges that are incident on a
single vertex or at least one of the vertices, irrespective of the edge
directions.

The `%--%`

operator can be used to select edges between
specific groups of vertices, ignoring edge directions in directed
graphs. For instance, the following expression selects all the edges
between Carmina (vertex index 3), Nang (vertex index 5) and Samira
(vertex index 6):

```
## + 3/9 edges from fddc1b6 (vertex names):
## [1] Carmina--Nang Carmina--Samira Nang --Samira
```

To make the `%--%`

operator work with names, you can build
string vectors containing the names and then use these vectors as
operands. For instance, to select all the edges that connect men to
women, we can do the following after re-adding the gender attribute that
we deleted earlier:

`## [1] "Bruno" "Moshe" "Nang" "Ibrahim"`

`## [1] "Alejandra" "Carmina" "Samira"`

```
## + 5/9 edges from fddc1b6 (vertex names):
## [1] Alejandra--Bruno Alejandra--Moshe Carmina --Moshe Carmina --Nang
## [5] Nang --Samira
```

The adjacency matrix is another way to represent a graph. In an
adjacency matrix, rows and columns are labeled by graph vertices, and
the elements of the matrix indicate the number of edges between vertices
*i* and *j*. The adjacency matrix for the example graph
is:

```
## 7 x 7 sparse Matrix of class "dgCMatrix"
## Alejandra Bruno Carmina Moshe Nang Samira Ibrahim
## Alejandra . 1 1 1 . . .
## Bruno 1 . . . . . .
## Carmina 1 . . 1 1 1 .
## Moshe 1 . 1 . . . 1
## Nang . . 1 . . 1 1
## Samira . . 1 . 1 . .
## Ibrahim . . . 1 1 . .
```

For example, Carmina (`1, 0, 0, 1, 1, 1, 0`

) is directly
connected to Alejandra (who has vertex index 1), Moshe (index 4), Nang
(index 5) and Samira (index 6), but not to Bruno (index 2) or to Ibrahim
(index 7).

A graph is an abstract mathematical object without a specific representation in 2D, 3D or any other geometric space. This means that whenever we want to visualise a graph, we have to find a mapping from vertices to coordinates in two- or three-dimensional space first, preferably in a way that is useful and/or pleasing for the eye. A separate branch of graph theory, namely graph drawing, tries to solve this problem via several graph layout algorithms. igraph implements quite a few layout algorithms and is also able to draw them onto the screen or to any output format that R itself supports.

The layout functions in igraph always start with `layout`

.
The following table summarises them:

Method name | Algorithm description |
---|---|

`layout_randomly` |
Places the vertices completely randomly |

`layout_in_circle` |
Deterministic layout that places the vertices on a circle |

`layout_on_sphere` |
Deterministic layout that places the vertices evenly on the surface of a sphere |

`layout_with_drl` |
The Drl (Distributed Recursive Layout) algorithm for large graphs |

`layout_with_fr` |
Fruchterman-Reingold force-directed algorithm |

`layout_with_kk` |
Kamada-Kawai force-directed algorithm |

`layout_with_lgl` |
The LGL (Large Graph Layout) algorithm for large graphs |

`layout_as_tree` |
Reingold-Tilford tree layout, useful for (almost) tree-like graphs |

`layout_nicely` |
Layout algorithm that automatically picks one of the other algorithms based on certain properties of the graph |

Layout algorithms can be called directly with a graph as its first argument. They will return a matrix with two columns and as many rows as the number of vertices in the graph; each row will correspond to the position of a single vertex, ordered by vertex IDs. Some algorithms have a 3D variant; in this case they return 3 columns instead of 2.

Some layout algorithms take additional arguments; for instance, when laying out a graph as a tree, it might make sense to specify which vertex is to be placed at the root of the layout:

We can plot our imaginary social network with the Kamada-Kawai layout algorithm as follows:

This should open a new window showing a visual representation of the network. Remember that the exact placement of nodes may be different on your machine since the layout is not deterministic.

The `layout`

argument also accepts functions; in this
case, the function will be called with the graph as its first argument.
This makes it possible to just pass the name of a layout function
directly, without creating a layout variable:

```
plot(
g,
layout = layout_with_fr,
main = "Social network with the Fruchterman-Reingold layout algorithm"
)
```

To improve the visuals, a trivial addition would be to color the vertices according to the gender. We should also try to place the labels slightly outside the vertices to improve readability:

```
V(g)$color <- ifelse(V(g)$gender == "m", "yellow", "red")
plot(
g,
layout = layout, vertex.label.dist = 3.5,
main = "Social network - with genders as colors"
)
```

You can also treat the `gender`

attribute as a factor and
provide the colors with an argument to `plot()`

, which takes
precedence over the `color`

vertex attribute. Colors will be
assigned automatically to levels of a factor:

As seen above with the `vertex.color`

argument, you can
specify visual properties as arguments to `plot`

instead of
using vertex or edge attributes. The following plot shows the formal
ties with thick lines while informal ones with thin lines:

```
plot(g,
layout = layout, vertex.label.dist = 3.5, vertex.size = 20,
vertex.color = ifelse(V(g)$gender == "m", "yellow", "red"),
edge.width = ifelse(E(g)$is_formal, 5, 1)
)
```

This latter approach is preferred if you want to keep the properties of the visual representation of your graph separate from the graph itself.

In summary, there are special vertex and edge properties that correspond to the visual representation of the graph. These attributes override the default settings of igraph (i.e color, weight, name, shape, layout, etc.). The following two tables summarise the most frequently used visual attributes for vertices and edges, respectively:

Attribute name | Keyword argument | Purpose |
---|---|---|

`color` |
`vertex.color` |
Color of the vertex |

`label` |
`vertex.label` |
Label of the vertex. They will be converted to character. Specify NA to omit vertex labels. The default vertex labels are the vertex ids. |

`label.cex` |
`vertex.label.cex` |
Font size of the vertex label, interpreted as a multiplicative
factor, similarly to R’s `text` function |

`label.color` |
`vertex.label.color` |
Color of the vertex label |

`label.degree` |
`vertex.label.degree` |
It defines the position of the vertex labels, relative to the center of the vertices. It is interpreted as an angle in radian, zero means ‘to the right’, and ‘pi’ means to the left, up is -pi/2 and down is pi/2. The default value is -pi/4 |

`label.dist` |
`vertex.label.dist` |
Distance of the vertex label from the vertex itself, relative to the vertex size |

`label.family` |
`vertex.label.family` |
Font family of the vertex, similarly to R’s `text`
function |

`label.font` |
`vertex.label.font` |
Font within the font family of the vertex, similarly to R’s
`text` function |

`shape` |
`vertex.shape` |
The shape of the vertex, currently “circle”, “square”, “csquare”, “rectangle”, “crectangle”, “vrectangle”, “pie” (see vertex.shape.pie), ‘sphere’, and “none” are supported, and only by the plot.igraph command. |

`size` |
`vertex.size` |
The size of the vertex, a numeric scalar or vector, in the latter case each vertex sizes may differ |

Attribute name | Keyword argument | Purpose |
---|---|---|

`color` |
`edge.color` |
Color of the edge |

`curved` |
`edge.curved` |
A numeric value specifies the curvature of the edge; zero curvature means straight edges, negative values means the edge bends clockwise, positive values the opposite. TRUE means curvature 0.5, FALSE means curvature zero |

`arrow.size` |
`edge.arrow.size` |
Currently this is a constant, so it is the same for every edge. If a vector is submitted then only the first element is used, that is to say if this is taken from an edge attribute then only the attribute of the first edge is used for all arrows. |

`arrow.width` |
`edge.arrow.width` |
The width of the arrows. Currently this is a constant, so it is the same for every edge |

`width` |
`edge.width` |
Width of the edge in pixels |

`label` |
`edge.label` |
If specified, it adds a label to the edge. |

`label.cex` |
`edge.label.cex` |
Font size of the edge label, interpreted as a multiplicative factor,
similarly to R’s `text` function |

`label.color` |
`edge.label.color` |
Color of the edge label |

`label.family` |
`edge.label.family` |
Font family of the edge, similarly to R’s `text`
function |

`label.font` |
`edge.label.font` |
Font within the font family of the edge, similarly to R’s
`text` function |

`plot()`

These settings can be specified as arguments to the `plot`

function to control the overall appearance of the plot.

Keyword argument | Purpose |
---|---|

`layout` |
The layout to be used. It can be an instance of `Layout` ,
a list of tuples containing X-Y coordinates, or the name of a layout
algorithm. The default is `auto` , which selects a layout
algorithm automatically based on the size and connectedness of the
graph. |

`margin` |
The amount of empty space below, over, at the left and right of the plot, it is a numeric vector of length four. |

No graph module would be complete without some kind of import/export
functionality that enables the package to communicate with external
programs and toolkits. `igraph`

is no exception: it provides
functions to read the most common graph formats and to save graphs into
files obeying these format specifications. The main functions for
reading and writing from/to file are `read_graph()`

and
`write_graph()`

, respectively. The following table summarises
the formats igraph can read or write:

Format | Short name | Read function | Write function |
---|---|---|---|

Adjacency list (a.k.a. LGL) | `lgl` |
`read_graph(file, format = c("lgl"))` |
`write_graph(graph, file, format = c("lgl"))` |

Adjacency matrix | `adjacency` |
`graph_from_adjacency_matrix(adjmatrix, mode = c("directed", "undirected", "max", "min", "upper","lower", "plus"), weighted = NULL, diag = TRUE, add.colnames = NULL, add.rownames = NA)` |
`as.matrix(graph, "adjacency")` |

DIMACS | `dimacs` |
`read_graph(file, format = c("dimacs"))` |
`write_graph(graph, file, format = c("dimacs"))` |

Edge list | `edgelist` |
`read_graph(file, format = c("edgelist"))` |
`write_graph(graph, file, format = c("edgelist"))` |

GraphViz | `dot` |
not supported yet | `write_graph(graph, file, format = c("dot"))` |

GML | `gml` |
`read_graph(file, format = c("gml"))` |
`write_graph(graph, file, format = c("gml"))` |

GraphML | `graphml` |
`read_graph(file, format = c("graphml"))` |
`write_graph(graph, file, format = c("graphml"))` |

LEDA | `leda` |
not supported yet | `write_graph(graph, file, format = c("leda"))` |

Labeled edgelist (a.k.a. NCOL) | `ncol` |
`read_graph(file, format = c("ncol"))` |
`write_graph(graph, file, format = c("ncol"))` |

Pajek format | `pajek` |
`read_graph(file, format = c("pajek"))` |
`write_graph(graph, file, format = c("pajek"))` |

**NOTE:** Each file format has its own limitations. For
instance, not all of them can store attributes. Your best bet is
probably GraphML or GML if you want to save igraph graphs in a format
that can be read from an external package and you want to preserve
numeric and string attributes. Edge list and NCOL is also fine if you
don’t have attributes (NCOL supports vertex names and edge weights,
though).

This tutorial is a brief introduction to `igraph`

in R. We
sincerely hope you enjoyed reading it and that it will be useful for
your own network analyses.

For a detailed description of specific functions, see https://r.igraph.org/reference/. For questions on how to
use `igraph`

, please visit our Forum. To report a bug, open a
Github issue.
Please do not ask usage questions on Github directly as it’s meant for
developers rather than users.

For the sake of reproducibility, the session information for the code above is the following:

```
## R version 4.3.2 (2023-10-31)
## Platform: aarch64-apple-darwin20 (64-bit)
## Running under: macOS Sonoma 14.3.1
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
##
## locale:
## [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: Europe/Zurich
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] igraph_2.0.3
##
## loaded via a namespace (and not attached):
## [1] digest_0.6.34 R6_2.5.1 fastmap_1.1.1 Matrix_1.6-5
## [5] xfun_0.42 lattice_0.22-5 magrittr_2.0.3 cachem_1.0.8
## [9] knitr_1.45 pkgconfig_2.0.3 htmltools_0.5.7 rmarkdown_2.26
## [13] lifecycle_1.0.4 cli_3.6.2 grid_4.3.2 sass_0.4.8
## [17] jquerylib_0.1.4 compiler_4.3.2 highr_0.10 rstudioapi_0.15.0
## [21] tools_4.3.2 evaluate_0.23 bslib_0.6.1 yaml_2.3.8
## [25] rlang_1.1.3 jsonlite_1.8.8
```