Query and manipulate a graph as it were an adjacency matrix

## Usage

```
# S3 method for igraph
[(
x,
i,
j,
...,
from,
to,
sparse = igraph_opt("sparsematrices"),
edges = FALSE,
drop = TRUE,
attr = if (is_weighted(x)) "weight" else NULL
)
```

## Arguments

- x
The graph.

- i
Index. Vertex ids or names or logical vectors. See details below.

- j
Index. Vertex ids or names or logical vectors. See details below.

- ...
Currently ignored.

- from
A numeric or character vector giving vertex ids or names. Together with the

`to`

argument, it can be used to query/set a sequence of edges. See details below. This argument cannot be present together with any of the`i`

and`j`

arguments and if it is present, then the`to`

argument must be present as well.- to
A numeric or character vector giving vertex ids or names. Together with the

`from`

argument, it can be used to query/set a sequence of edges. See details below. This argument cannot be present together with any of the`i`

and`j`

arguments and if it is present, then the`from`

argument must be present as well.- sparse
Logical scalar, whether to return sparse matrices.

- edges
Logical scalar, whether to return edge ids.

- drop
Ignored.

- attr
If not

`NULL`

, then it should be the name of an edge attribute. This attribute is queried and returned.

## Details

The single bracket indexes the (possibly weighted) adjacency matrix of the graph. Here is what you can do with it:

Check whether there is an edge between two vertices (\(v\) and \(w\)) in the graph:

`graph[v, w]`

A numeric scalar is returned, one if the edge exists, zero otherwise.

Extract the (sparse) adjacency matrix of the graph, or part of it:

`graph[] graph[1:3,5:6] graph[c(1,3,5),]`

The first variants returns the full adjacency matrix, the other two return part of it.

The

`from`

and`to`

arguments can be used to check the existence of many edges. In this case, both`from`

and`to`

must be present and they must have the same length. They must contain vertex ids or names. A numeric vector is returned, of the same length as`from`

and`to`

, it contains ones for existing edges edges and zeros for non-existing ones. Example:`graph[from=1:3, to=c(2,3,5)]`

.

For weighted graphs, the

`[`

operator returns the edge weights. For non-esistent edges zero weights are returned. Other edge attributes can be queried as well, by giving the`attr`

argument.Querying edge ids instead of the existance of edges or edge attributes. E.g.

`graph[1, 2, edges=TRUE]`

returns the id of the edge between vertices 1 and 2, or zero if there is no such edge.

Adding one or more edges to a graph. For this the element(s) of the imaginary adjacency matrix must be set to a non-zero numeric value (or

`TRUE`

):`graph[1, 2] <- 1 graph[1:3,1] <- 1 graph[from=1:3, to=c(2,3,5)] <- TRUE`

This does not affect edges that are already present in the graph, i.e. no multiple edges are created.

Adding weighted edges to a graph. The

`attr`

argument contains the name of the edge attribute to set, so it does not have to be ‘weight’:If an edge is already present in the network, then only its weights or other attribute are updated. If the graph is already weighted, then the

`attr="weight"`

setting is implicit, and one does not need to give it explicitly.Deleting edges. The replacement syntax allow the deletion of edges, by specifying

`FALSE`

or`NULL`

as the replacement value:`graph[v, w] <- FALSE`

removes the edge from vertex \(v\) to vertex \(w\). As this can be used to delete edges between two sets of vertices, either pairwise:

`graph[from=v, to=w] <- FALSE`

or not:

`graph[v, w] <- FALSE`

if \(v\) and \(w\) are vectors of edge ids or names.

‘`[`

’ allows logical indices and negative indices as well,
with the usual R semantics. E.g.

` graph[degree(graph)==0, 1] <- 1`

adds an edge from every isolate vertex to vertex one, and

```
G <- make_empty_graph(10)
G[-1,1] <- TRUE
```

creates a star graph.

Of course, the indexing operators support vertex names,
so instead of a numeric vertex id a vertex can also be given to
‘`[`

’ and ‘`[[`

’.

## See also

Other structural queries:
`[[.igraph()`

,
`adjacent_vertices()`

,
`are_adjacent()`

,
`ends()`

,
`get.edge.ids()`

,
`gorder()`

,
`gsize()`

,
`head_of()`

,
`incident_edges()`

,
`incident()`

,
`is_directed()`

,
`neighbors()`

,
`tail_of()`