closeness_c: Closeness centrality

In wdnet: Weighted and Directed Networks

Closeness centrality

Description

Compute the closeness centrality measures of the vertices in a weighted and directed network represented through its adjacency matrix.

Usage

closeness_c(
  adj,
  alpha = 1,
  mode = "out",
  method = "harmonic",
  distance = FALSE
)

Arguments

adj	is an adjacency matrix of a weighted and directed network
alpha	is a tuning parameter. The value of alpha must be nonnegative. By convention, alpha takes a value from 0 to 1 (default).
mode	which mode to compute: "out" (default) or "in"? For undirected networks, this setting is irrelevant.
method	which method to use: "harmonic" (default) or "standard"?
distance	whether to consider the entries in the adjacency matrix as distances or strong connections. The default setting is FALSE.

Value

a list of node names and associated closeness centrality measures

Note

Function closeness_c is an extension of function closeness in package igraph and function closeness_w in package tnet. The method of computing distances between vertices is the Dijkstra's algorithm.

References

• Dijkstra, E.W. (1959). A note on two problems in connexion with graphs. Numerische Mathematik, 1, 269–271.

• Newman, M.E.J. (2003). The structure and function of complex networks. SIAM review, 45(2), 167–256.

• Opsahl, T., Agneessens, F., Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32, 245–251.

• Zhang, P., Zhao, J. and Yan, J. (2020+) Centrality measures of networks with application to world input-output tables

wdnet documentation built on May 29, 2024, 9:32 a.m.