igraph-package {igraph} | R Documentation |

The main goals of the igraph library is an open source is to provide a set of data types and functions for 1) pain-free implementation of graph algorithms, 2) fast handling of large graphs, with millions of vertices and edges, 3) allow rapid prototyping via high level languages like R.

There are many functions in igraph for creating graphs, both deterministic and stochastic; stochastic graph constructors are called ‘games’ is igraph.

To create small graphs with a given structure probably the
`graph.formula`

function is easiest. It uses R's formula
interface, its manual page contains many examples. Another option is
`graph`

, which takes numeric vertex ids directly.
`graph.atlas`

creates graph from the Graph Atlas,
`graph.famous`

can create some special graphs.

To create graphs from field data, `graph.edgelist`

,
`graph.data.frame`

and `graph.adjacency`

are
probably the best choices.

The igraph include some classic random graphs like the Erdos-Renyi GNP
and GNM graphs (`erdos.renyi.game`

) and some recent
popular models, like preferential attachment
(`barabasi.game`

) and the small-world model
(`watts.strogatz.game`

).

Vertices and edges have numerical vertex ids in igraph. Vertex ids are
always consecutive and they start with zero. I.e. for a graph with
‘n’ vertices the vertex ids are between ‘0’ and
‘n-1’. If some operation changes the number of vertices in the
graphs, e.g. a subgraph is created via `subgraph`

, then
the vertices are renumbered to satisfty this criteria.

The same is true for the edges as well, edge ids are always between zero and ‘m-1’ where ‘m’ is the total number of edges in the graph.

It is often desirable to follow vertices along a number of graph operations, and vertex ids don't allow this because of the renumbering. The solution is to assign attributes to the vertices. These are kept by all operations, if possible. See more about attributes in the next section.

In igraph it is possible to assign attributes to the vertices or edges
of a graph, or to the graph itself. igraph provides flexible
constructs for selecting a set of vertices or edges based on their
attribute values, see `get.vertex.attribute`

and
`iterators`

for details.

Some vertex/edge/graph attributes are treated specially. One of them
is the ‘name’ attribute. This is used for printing the graph
instead of the numerical ids, if it exists. Other attributes define
visualization parameters, see `igraph.plotting`

for
details.

Attribute values can be set to any R object, but note that storing the
graph in some file formats might result the loss of complex attribute
values. All attribute values are preserved if you use
`save`

and `load`

to store/retrieve your
graphs.

igraph provides three different ways for visualization. The first is
the `\layout{plot.igraph}`

function. (Actually you don't need to
write ‘`plot.igraph`

’, ‘`plot`

’ is
enough. This function uses base R graphic and can be used with any R
device.

The second function is `tkplot`

, which uses a Tk GUI for
basic interactive graph manipulation. (Tk is quite resource hungry, so
don't try this for very large graphs.)

The third way requires the `rgl`

package and uses OpenGL. See the
`rglplot`

function for the details.

Make sure you read `igraph.plotting`

before you start
plotting your graphs.

igraph can handle various graph file formats, usually both for reading
and writing. We suggest that you use the GraphML file format for your
graphs, except if the graphs are too big. For big graphs a simpler
format is recommended. See `read.graph`

and
`write.graph`

for details.

The igraph homepage is at http://cneurocvs.rmki.kfki.hu/igraph. See especially the documentation section. Join the igraph-help mailing list if you have questions or comments.

Gabor Csardi csardi@rmki.kfki.hu

[Package *igraph* version 0.5.1 Index]