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
)

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: A character scalar that gives the name of the edge attribute to use in the adjacency matrix. If it is NULL, then the ‘weight’ edge attribute of the graph is used, if there is one. Otherwise, or if it is NA, then the calculation uses the standard adjacency 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