VoteRank Centrality

Identifies influential spreaders via an iterative voting mechanism. Returns normalized rank (1 = most influential). Based on Zhang et al. (2016).

Usage

centrality_voterank(x, ...)

Arguments

x

Network input (matrix, igraph, network, cograph_network, tna object).

...

Additional arguments passed to centrality (e.g., weighted, directed).

Value

Named numeric vector of VoteRank values.

See also

centrality for computing multiple measures at once.

Examples

adj <- matrix(c(0, 1, 1, 1, 0, 1, 1, 1, 0), 3, 3)
rownames(adj) <- colnames(adj) <- c("A", "B", "C")
centrality_voterank(adj)
#>         A         B         C 
#> 1.0000000 0.6666667 0.3333333