Convert a graph to an adjacency matrix

Sometimes it is useful to work with a standard representation of a graph, like an adjacency matrix.

Usage

as_adjacency_matrix(
  graph,
  type = c("both", "upper", "lower"),
  ...,
  weights = NULL,
  names = TRUE,
  sparse = igraph_opt("sparsematrices"),
  edges = deprecated(),
  attr = deprecated()
)

Arguments

graph

The graph to convert.

type

Gives how to create the adjacency matrix for undirected graphs. It is ignored for directed graphs. Possible values: upper: the upper right triangle of the matrix is used, lower: the lower left triangle of the matrix is used. both: the whole matrix is used, a symmetric matrix is returned.

...

These dots are for future extensions and must be empty.

weights

One of the following:

• NULL (default): use the weight edge attribute if the graph has one, otherwise return a traditional (unweighted) adjacency matrix.

• NA: explicitly unweighted, ignoring any weight edge attribute.

• A numeric or logical vector of length ecount(): use these values directly as edge weights.

• A character scalar: the name of an edge attribute whose values are used as weights. The attribute must be numeric or logical.

If multiple edges share endpoints, the value of an arbitrarily chosen edge is included in the matrix.

names

Logical constant, whether to assign row and column names to the matrix. These are only assigned if the name vertex attribute is present in the graph.

sparse

Logical scalar, whether to create a sparse matrix. The ‘Matrix’ package must be installed for creating sparse matrices.

edges

Logical scalar, whether to return the edge IDs in the matrix. For non-existant edges zero is returned.

attr

Use weights instead. If supplied, the value is forwarded to weights as a character edge attribute name.

Value

A vcount(graph) by vcount(graph) (usually) numeric matrix.

Details

as_adjacency_matrix() returns the adjacency matrix of a graph, a regular matrix if sparse is FALSE, or a sparse matrix, as defined in the ‘Matrix’ package, if sparse if TRUE.

Related documentation in the C library

get_adjacency(), get_adjacency_sparse(), vcount(), edges(), get_eids(), ecount()

See also

graph_from_adjacency_matrix(), read_graph()

Other conversion: as.matrix.igraph(), as_adj_list(), as_biadjacency_matrix(), as_data_frame(), as_directed(), as_edgelist(), as_graphnel(), as_long_data_frame(), graph_from_adj_list(), graph_from_graphnel()

Examples

g <- sample_gnp(10, 2 / 10)
as_adjacency_matrix(g)
#> 10 x 10 sparse Matrix of class "dgCMatrix"
#>                          
#>  [1,] . . . . . . . . . .
#>  [2,] . . . . . 1 . 1 . .
#>  [3,] . . . . . . 1 . . .
#>  [4,] . . . . . . 1 . . 1
#>  [5,] . . . . . 1 . 1 . 1
#>  [6,] . 1 . . 1 . 1 . . 1
#>  [7,] . . 1 1 . 1 . . . 1
#>  [8,] . 1 . . 1 . . . . 1
#>  [9,] . . . . . . . . . 1
#> [10,] . . . 1 1 1 1 1 1 .
V(g)$name <- letters[1:vcount(g)]
as_adjacency_matrix(g)
#> 10 x 10 sparse Matrix of class "dgCMatrix"
#>   [[ suppressing 10 column names ‘a’, ‘b’, ‘c’ ... ]]
#>                      
#> a . . . . . . . . . .
#> b . . . . . 1 . 1 . .
#> c . . . . . . 1 . . .
#> d . . . . . . 1 . . 1
#> e . . . . . 1 . 1 . 1
#> f . 1 . . 1 . 1 . . 1
#> g . . 1 1 . 1 . . . 1
#> h . 1 . . 1 . . . . 1
#> i . . . . . . . . . 1
#> j . . . 1 1 1 1 1 1 .
E(g)$weight <- runif(ecount(g))
as_adjacency_matrix(g)
#> 10 x 10 sparse Matrix of class "dgCMatrix"
#>   [[ suppressing 10 column names ‘a’, ‘b’, ‘c’ ... ]]
#>                                                                                
#> a . .         .          .         .           .           .          .        
#> b . .         .          .         .           0.330121691 .          0.8328456
#> c . .         .          .         .           .           0.07899829 .        
#> d . .         .          .         .           .           0.62337103 .        
#> e . .         .          .         .           0.009822477 .          0.4678542
#> f . 0.3301217 .          .         0.009822477 .           0.35373960 .        
#> g . .         0.07899829 0.6233710 .           0.353739604 .          .        
#> h . 0.8328456 .          .         0.467854201 .           .          .        
#> i . .         .          .         .           .           .          .        
#> j . .         .          0.9239261 0.316179677 0.823120607 0.78203059 0.8823195
#>                      
#> a .         .        
#> b .         .        
#> c .         .        
#> d .         0.9239261
#> e .         0.3161797
#> f .         0.8231206
#> g .         0.7820306
#> h .         0.8823195
#> i .         0.3621016
#> j 0.3621016 .        
as_adjacency_matrix(g, weights = NA)
#> 10 x 10 sparse Matrix of class "dgCMatrix"
#>   [[ suppressing 10 column names ‘a’, ‘b’, ‘c’ ... ]]
#>                      
#> a . . . . . . . . . .
#> b . . . . . 1 . 1 . .
#> c . . . . . . 1 . . .
#> d . . . . . . 1 . . 1
#> e . . . . . 1 . 1 . 1
#> f . 1 . . 1 . 1 . . 1
#> g . . 1 1 . 1 . . . 1
#> h . 1 . . 1 . . . . 1
#> i . . . . . . . . . 1
#> j . . . 1 1 1 1 1 1 .