alpha.centrality: Find Bonacich alpha centrality scores of network positions
In igraph: Network Analysis and Visualization

alpha.centrality

R Documentation

Find Bonacich alpha centrality scores of network positions

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

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