| closeness.freeman {centiserve} | R Documentation |
Find the closeness centrality in a strongly connected graph
Description
Freeman closeness centrality defined as:
\frac{1}{\sum_{i\neq v}d(v,i)}
Usage
closeness.freeman(graph, vids = V(graph), mode = c("all", "out", "in"),
weights = NULL, normalized = FALSE)
Arguments
graph |
The input graph as igraph object |
vids |
Vertex sequence, the vertices for which the centrality values are returned. Default is all vertices. |
mode |
Character string, defined the types of the paths used for measuring the distance in directed graphs. 'in' measures the paths to a vertex, 'out' measures paths from a vertex, all uses undirected paths. This argument is ignored for undirected graphs. |
weights |
Possibly a numeric vector giving edge weights. If this is NULL, the default, and the graph has a weight edge attribute, then the attribute is used. If this is NA then no weights are used (even if the graph has a weight attribute). |
normalized |
Logical scalar, whether to calculate the normalized score. |
Details
Because closeness is infinite if there is no path between two vertex so freeman closeness require a strongly connected graph. In igraph if there is no (directed) path between vertex v and i then the total number of vertices is used in the formula instead of the path length.
More detail at Closeness Centrality
Value
A numeric vector contaning the centrality scores for the selected vertices.
Author(s)
Mahdi Jalili m_jalili@farabi.tums.ac.ir
Use igraph package closeness function.
References
Freeman, Linton C. "Centrality in social networks conceptual clarification." Social networks 1.3 (1979): 215-239.
Examples
g <- graph(c(1,2,2,3,3,4,4,2), directed=FALSE)
closeness.freeman(g)