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