calculate_centralities: Centrality measure calculation

In CINNA: Deciphering Central Informative Nodes in Network Analysis

Centrality measure calculation

Description

This function computes multitude centrality measures of an igraph object.

Usage

calculate_centralities(x, except = NULL, include = NULL, weights = NULL)

Arguments

x	the component of a network as an igraph object
except	A vector containing names of centrality measures which could be omitted from the calculations.
include	A vector including names of centrality measures which should be computed.
weights	A character scalar specifying the edge attribute to use. (default=NULL)

Details

This function calculates various types of centrality measures which are applicable to the network topology and returns the results as a list. In the "except" argument, you can specify centrality measures that are not necessary to calculate.

Value

A list containing centrality measure values. Each column indicates a centrality measure, and each row corresponds to a vertex. The structure of the output is as follows: - The list has named elements, where each element represents a centrality measure. - The value of each element is a numeric vector, where each element of the vector corresponds to a vertex in the network. - The order of vertices in the numeric vectors matches the order of vertices in the input igraph object. - The class of the output is "centrality".

Author(s)

Minoo Ashtiani, Mehdi Mirzaie, Mohieddin Jafari

References

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See Also

alpha.centrality, bonpow, constraint, centr_degree, eccentricity, eigen_centrality, coreness, authority_score, hub_score, transitivity, page_rank, betweenness, subgraph.centrality, flowbet, infocent, loadcent, stresscent, graphcent, topocoefficient, closeness.currentflow, closeness.latora, communibet, communitycent, crossclique, entropy, epc, laplacian, leverage, mnc, hubbell, semilocal, closeness.vitality, closeness.residual, lobby, markovcent, radiality, lincent, geokpath, katzcent, diffusion.degree, dmnc, centroid, closeness.freeman, clusterrank, decay, barycenter, bottleneck, averagedis, local_bridging_centrality, wiener_index_centrality, group_centrality, dangalchev_closeness_centrality, harmonic_centrality, strength

Examples

data("zachary")
p <- proper_centralities(zachary)
calculate_centralities(zachary, include = "Degree Centrality")

CINNA documentation built on Aug. 8, 2023, 5:13 p.m.