Find Bonacich alpha centrality scores of network positions

alpha.centrality() was renamed to alpha_centrality() to create a more consistent API.

Usage

alpha.centrality(
  graph,
  nodes = V(graph),
  alpha = 1,
  loops = FALSE,
  exo = 1,
  weights = NULL,
  tol = 1e-07,
  sparse = TRUE
)

Arguments

graph

The input graph, can be directed or undirected. In undirected graphs, edges are treated as if they were reciprocal directed ones.

nodes

Vertex sequence, the vertices for which the alpha centrality values are returned. (For technical reasons they will be calculated for all vertices, anyway.)

alpha

Parameter specifying the relative importance of endogenous versus exogenous factors in the determination of centrality. See details below.

loops

Whether to eliminate loop edges from the graph before the calculation.

exo

The exogenous factors, in most cases this is either a constant – the same factor for every node, or a vector giving the factor for every vertex. Note that too long vectors will be truncated and too short vectors will be replicated to match the number of vertices.

weights

One of the following:

• NULL (default): use the weight edge attribute if the graph has one, otherwise return a traditional (unweighted) adjacency matrix.

• NA: explicitly unweighted, ignoring any weight edge attribute.

• A numeric or logical vector of length ecount(): use these values directly as edge weights.

• A character scalar: the name of an edge attribute whose values are used as weights. The attribute must be numeric or logical.

If multiple edges share endpoints, the value of an arbitrarily chosen edge is included in the matrix.

tol

Tolerance for near-singularities during matrix inversion, see solve().

sparse

Logical scalar, whether to use sparse matrices for the calculation. The ‘Matrix’ package is required for sparse matrix support

Related documentation in the C library

vcount(), simplify(), get_adjacency(), get_adjacency_sparse(), is_simple(), edges(), get_eids(), ecount()