VoteRank Centrality

VoteRank (Zhang et al. 2016) elects spreaders one at a time. Every node votes for its neighbors with a voting ability that starts at 1, the node with the most votes is elected, and each neighbor of the elected node loses \(1/\langle k \rangle\) of its ability, where \(\langle k \rangle\) is the mean degree. A node elected in round \(r\) of \(m\) rounds scores \((m + 1 - r)/m\), so the first node elected scores 1.

Usage

centrality_voterank(x, ...)

Arguments

x

Network input accepted by centrality.

...

Further arguments to centrality.

Value

A named numeric vector with one score per node, in input node order.

Details

Edge weights are ignored. On a directed network a node receives the votes of its in-neighbors, the election reduces the ability of the out-neighbors of the elected node, and the mean degree counts in-ties and out-ties. Every node is elected in turn, so on \(n\) nodes the scores are \(1/n, 2/n, \ldots, 1\). A tie in votes goes to the node listed first.

References

Zhang, J.-X., Chen, D.-B., Dong, Q., & Zhao, Z.-D. (2016). Identifying a set of influential spreaders in complex networks. Scientific Reports, 6, 27823. doi:10.1038/srep27823 .

See also

centrality_ncvoterank, centrality_wvoterank, centrality.

Examples

centrality_voterank(regulation_net)
#>    Explore       Plan    Monitor      Adapt    Reflect    Discuss Synthesize 
#>        0.8        0.5        1.0        0.4        0.9        0.6        0.3 
#>   Evaluate     Create      Share 
#>        0.2        0.7        0.1