0th

Percentile

##### Create graphs from adjacency matrices

graph.adjacency is a flexible function for creating igraph graphs from adjacency matrices.

Keywords
graphs
##### Usage
graph.adjacency(adjmatrix, mode=c("directed", "undirected", "max",
"min", "upper", "lower", "plus"), weighted=NULL, diag=TRUE,
add.colnames=NULL, add.rownames=NA)
##### Arguments
A square adjacency matrix. From igraph version 0.5.1 this can be a sparse matrix created with the Matrix package.
mode
Character scalar, specifies how igraph should interpret the supplied matrix. See also the weighted argument, the interpretation depends on that too. Possible values are: directed, undirected, upper<
weighted
This argument specifies whether to create a weighted graph from an adjacency matrix. If it is NULL then an unweighted graph is created and the elements of the adjacency matrix gives the number of edges between the vertices. If it
diag
Logical scalar, whether to include the diagonal of the matrix in the calculation. If this is FALSE then the diagonal is zero-d out first.
Character scalar, whether to add the column names as vertex attributes. If it is NULL (the default) then, if present, column names are added as vertex attribute name. If NA
Character scalar, whether to add the row names as vertex attributes. Possible values the same as the previous argument. By default row names are not added. If add.rownames and add.colnames
##### Details

graph.adjacency creates a graph from an adjacency matrix.

The order of the vertices are preserved, i.e. the vertex corresponding to the first row will be vertex 0 in the graph, etc.

graph.adjacency operates in two main modes, depending on the weighted argument.

If this argument is NULL then an unweighted graph is created and an element of the adjacency matrix gives the number of edges to create between the two corresponding vertices. The details depend on the value of the mode argument:

• directed
{The graph will be directed and a matrix element gives the number of edges between two vertices.} undirected{This is exactly the same as max, for convenience. Note that it is not checked whether the matrix is symmetric.} max{An undirected graph will be created and max(A(i,j), A(j,i)) gives the number of edges.} upper{An undirected graph will be created, only the upper right triangle (including the diagonal) is used for the number of edges.} lower{An undirected graph will be created, only the lower left triangle (including the diagonal) is used for creating the edges.} min{undirected graph will be created with min(A(i,j), A(j,i)) edges between vertex i and j.} plus{ undirected graph will be created with A(i,j)+A(j,i) edges between vertex i and j.}

##### Value

• An igraph graph object.

##### concept

• Sparse matrix

##### code

A(i,j)+A(j,i)

##### itemize

• directed

##### item

• undirected
• max
• upper
• lower
• min
• plus

graph and graph.formula for other ways to create graphs.

##### Examples
adjm <- matrix(sample(0:1, 100, replace=TRUE, prob=c(0.9,0.1)), nc=10)
prob=c(0.9,0.02,0.02,0.02,0.02,0.02)), nc=10)
E(g2)$weight ## various modes for weighted graphs, with some tests nzs <- function(x) sort(x [x!=0]) adjm <- matrix(runif(100), 10) adjm[ adjm<0.5 ] <- 0 g3 <- graph.adjacency((adjm + t(adjm))/2, weighted=TRUE, mode="undirected") g4 <- graph.adjacency(adjm, weighted=TRUE, mode="max") all(nzs(pmax(adjm, t(adjm))[upper.tri(adjm)]) == sort(E(g4)$weight))

all(nzs(pmin(adjm, t(adjm))[upper.tri(adjm)]) == sort(E(g5)$weight)) g6 <- graph.adjacency(adjm, weighted=TRUE, mode="upper") all(nzs(adjm[upper.tri(adjm)]) == sort(E(g6)$weight))

all(nzs(adjm[lower.tri(adjm)]) == sort(E(g7)$weight)) g8 <- graph.adjacency(adjm, weighted=TRUE, mode="plus") d2 <- function(x) { diag(x) <- diag(x)/2; x } all(nzs((d2(adjm+t(adjm)))[lower.tri(adjm)]) == sort(E(g8)$weight))

summary(g10)