Apply Layout to Network

Apply a layout algorithm to compute node positions.

Usage

sn_layout(network, layout, seed = 42, ...)

Arguments

network

A cograph_network object, matrix, data.frame, or igraph object. Matrices and other inputs are auto-converted.

layout

Layout algorithm name or a CographLayout object.

seed

Random seed for deterministic layouts. Default 42. Set NULL for random.

...

Additional arguments passed to the layout function.

Value

Modified cograph_network object.

Details

Built-in Layouts

spring

Force-directed layout (Fruchterman-Reingold style). Good general-purpose layout. Default.

circle

Nodes arranged in a circle. Good for small networks or when structure is less important.

groups

Circular layout with grouped nodes clustered together.

grid

Nodes in a regular grid.

random

Random positions. Useful as starting point.

star

Central node with others arranged around it.

bipartite

Two-column layout for bipartite networks.

igraph Layouts

Two-letter codes for igraph layouts: "kk" (Kamada-Kawai), "fr" (Fruchterman-Reingold), "drl", "mds", "ni" (nicely), "tr" (tree), "ci" (circle), etc.

You can also pass igraph layout functions directly or use full names like "layout_with_kk".

See also

cograph for network creation, sn_nodes for node customization, sn_edges for edge customization, sn_theme for visual themes, splot and soplot for plotting

Examples

adj <- matrix(c(0, 1, 1, 1, 0, 1, 1, 1, 0), nrow = 3)

# Built-in layouts
cograph(adj) |> sn_layout("circle") |> splot()

cograph(adj) |> sn_layout("spring") |> splot()


# igraph layouts (if igraph installed)
if (requireNamespace("igraph", quietly = TRUE)) {
  cograph(adj) |> sn_layout("kk") |> splot()
  cograph(adj) |> sn_layout("fr") |> splot()
}



# Custom coordinates
coords <- matrix(c(0, 0, 1, 0, 0.5, 1), ncol = 2, byrow = TRUE)
cograph(adj) |> sn_layout(coords) |> splot()


# Direct matrix input (auto-converts)
adj |> sn_layout("circle")
#> Cograph network: 3 nodes, 3 edges ( undirected )
#> Source: matrix 
#>   Nodes (3): 1, 2, 3
#>   Edges: 3 / 3 (density: 100.0%)
#>   Weights: [1.000, 1.000]  |  mean: 1.000
#>   Strongest edges:
#>     1 -- 2  1.000
#>     1 -- 3  1.000
#>     2 -- 3  1.000
#> Layout: set