| clusterrank {centiserve} | R Documentation |
Find the ClusterRank ranks in a graph
Description
Mathematically, the ClusterRank score s_{i} of node i is defined as:
s_{i} = f(c_{i})\sum_{j\in \tau _{i}}(k_{out}^{j}+1)
where the term f(c_i) accounts for the effect of i's local clustering and the term '+1' results from the contribution of j itself.
Here f(c_{i}) = 10^{-c_{i}}
Usage
clusterrank(graph, vids = V(graph), directed = TRUE, loops = TRUE)
Arguments
graph |
The input graph as igraph object |
vids |
Vertex sequence, the vertices for which the centrality values are returned. Default is all vertices. |
directed |
Logical scalar, whether to directed graph is analyzed. This argument is ignored for undirected graphs. |
loops |
Logical; whether the loop edges are also counted. |
Details
ClusterRank is a local ranking algorithm which takes into account not only the number of neighbors and the neighbors' influences, but also the clustering coefficient.
ClusterRank can also be applied to undirected networks where the superiority of ClusterRank is significant compared with degree centrality and k-core decomposition.
More detail at ClusterRank
Value
A numeric vector contaning the centrality scores for the selected vertices.
Author(s)
Mahdi Jalili m_jalili@farabi.tums.ac.ir
References
Chen, Duan-Bing, et al. "Identifying influential nodes in large-scale directed networks: the role of clustering." PloS one 8.10 (2013): e77455.
Examples
g <- graph(c(1,2,2,3,3,4,4,2,2,5,5,3,4,1,4,3,1,6,6,3,3,6,2,6,5,6))
clusterrank(g)