R: Centralize a graph according to the eigenvector centrality of...

centr_eigen {igraph} | R Documentation

## Centralize a graph according to the eigenvector centrality of vertices

### Description

See `centralize` for a summary of graph centralization.

### Usage

    centr_eigen(
      graph,
      directed = FALSE,
      scale = TRUE,
      options = arpack_defaults,
      normalized = TRUE
    )

### Arguments

`graph` | The input graph.
`directed` | logical scalar, whether to use directed shortest paths for calculating eigenvector centrality.
`scale` | Whether to rescale the eigenvector centrality scores, such that the maximum score is one.
`options` | This is passed to `eigen_centrality`, the options for the ARPACK eigensolver.
`normalized` | Logical scalar. Whether to normalize the graph level centrality score by dividing by the theoretical maximum.

### Value

A named list with the following components:

`vector` | The node-level centrality scores.
`value` | The corresponding eigenvalue.
`options` | ARPACK options, see the return value of `eigen_centrality` for details.
`centralization` | The graph level centrality index.
`theoretical_max` | The same as above, the theoretical maximum centralization score for a graph with the same number of vertices.

### See Also

Other centralization related: `centr_betw_tmax()`, `centr_betw()`, `centr_clo_tmax()`, `centr_clo()`, `centr_degree_tmax()`, `centr_degree()`, `centr_eigen_tmax()`, `centralize()`

### Examples

    # A BA graph is quite centralized
    g <- sample_pa(1000, m = 4)
    centr_degree(g)$centralization
    centr_clo(g, mode = "all")$centralization
    centr_betw(g, directed = FALSE)$centralization
    centr_eigen(g, directed = FALSE)$centralization

    # The most centralized graph according to eigenvector centrality
    g0 <- make_graph(c(2,1), n = 10, dir = FALSE)
    g1 <- make_star(10, mode = "undirected")
    centr_eigen(g0)$centralization
    centr_eigen(g1)$centralization

[Package igraph version 1.3.2 Index]