markovcent: Find the markov centrality score

In centiserve: Find Graph Centrality Indices

Description

The Markov centrality score uses the concept of a random walk through the graph to calculate the centrality of each vertex.

Usage

markovcent(graph, vids = V(graph))

Arguments

graph	The input graph as igraph object
vids	Vertex sequence, the vertices for which the markov centrality values are returned.

Details

The method uses the mean first-passage time from every vertex to every other vertex to produce a score for each vertex.
More detail at Markov Centrality

Value

A numeric vector contaning the centrality scores for the selected vertices.

Author(s)

Mahdi Jalili [e-mail]

Original code from Bioconductor SANTA package (Cornish AJ, 2014)

References

White, S. & Smyth, P. Algorithms for estimating relative importance in networks. Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, 2003. ACM, 266-275.

Cornish AJ and Markowetz F (2014). "SANTA: Quantifying the Functional Content of Molecular Networks." PLOS Computational Biology, 10(9), pp. e1003808. http://dx.doi.org/10.1371/journal.pcbi.1003808.

Examples

g <- graph(c(1,2,2,3,3,4,4,2))
markovcent(g)

Example output

Loading required package: igraph

Attaching package: 'igraph'

The following objects are masked from 'package:stats':

    decompose, spectrum

The following object is masked from 'package:base':

    union

Loading required package: Matrix
[1] -1.850372e-17  1.000000e+00  8.000000e-01  6.666667e-01

centiserve documentation built on May 2, 2019, 6:01 a.m.