hubbell {centiserve} | R Documentation |
Hubbell centrality defined as:
C(h) = E + WC(h)
where E is some exogeneous input and w is a weight matrix derived from the adjancancy matrix A.
hubbell(graph, vids = V(graph), weights = NULL, weightfactor = 0.5)
graph |
The input graph as igraph object |
vids |
Vertex sequence, the vertices for which the centrality values are returned. Default is all vertices. |
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). |
weightfactor |
The weight factorLogical which must be greater than 0. The defualt is 0.5. |
This centrality value is defined by means of a weighted and loop allowed network. The weighted adjacency matrix w of a network G is asymmetric and contains real-valued weights for each edge.
More detail at Hubbell Index
A numeric vector contaning the centrality scores for the selected vertices.
Mahdi Jalili m_jalili@farabi.tums.ac.ir
Algorithm adapted from CentiLib (Grabler, Johannes, 2012).
Hubbell, Charles H. "An input-output approach to clique identification." Sociometry (1965): 377-399.
Grabler, Johannes, Dirk Koschutzki, and Falk Schreiber. "CentiLib: comprehensive analysis and exploration of network centralities." Bioinformatics 28.8 (2012): 1178-1179.
g <- barabasi.game(100) hubbell(g)