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Select Top N Nodes by Centrality

Source: R/network-utils.R
select_top.Rd

Select the top N nodes ranked by a centrality measure.

Usage

select_top(
  x,
  n,
  by = "degree",
  ...,
  .keep_edges = c("internal", "none"),
  keep_format = FALSE,
  directed = NULL
)

Arguments

x

Network input.

n

Integer. Number of top nodes to select.

by

Character. Centrality measure for ranking. One of: "degree", "indegree", "outdegree", "strength", "instrength", "outstrength", "betweenness", "closeness", "eigenvector", "pagerank", "hub", "authority", "coreness". Default "degree".

...

Additional filter expressions to apply.

.keep_edges

How to handle edges. Default "internal".

keep_format

Logical. Keep input format? Default FALSE.

directed

Logical or NULL. Auto-detect if NULL.

Value

A cograph_network with the top N nodes.

See also

select_nodes, select_component

Examples

adj <- matrix(c(0, .5, .8, 0,
                .5, 0, .3, .6,
                .8, .3, 0, .4,
                 0, .6, .4, 0), 4, 4, byrow = TRUE)
rownames(adj) <- colnames(adj) <- c("A", "B", "C", "D")

# Top 2 by degree
select_top(adj, n = 2)
#> Cograph network: 2 nodes, 1 edges ( undirected )
#> Source: filtered 
#>   Nodes (2): B, C
#>   Edges: 1 / 1 (density: 100.0%)
#>   Weights: [0.300, 0.300]  |  mean: 0.300
#>   Strongest edges:
#>     B -- C  0.300
#> Layout: none 

# Top 2 by PageRank
select_top(adj, n = 2, by = "pagerank")
#> Cograph network: 2 nodes, 1 edges ( undirected )
#> Source: filtered 
#>   Nodes (2): B, C
#>   Edges: 1 / 1 (density: 100.0%)
#>   Weights: [0.300, 0.300]  |  mean: 0.300
#>   Strongest edges:
#>     B -- C  0.300
#> Layout: none