clusterRank | R Documentation |

This function calculates the ClusterRank of input vertices and works with both directed and undirected networks. This function and all of its descriptions have been adapted from the centiserve package with some minor modifications. ClusterRank is a local ranking algorithm which takes into account not only the number of neighbors and the neighborsâ€™ influences, but also the clustering coefficient.

clusterRank(graph, vids = V(graph), directed = FALSE, loops = TRUE)

`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. |

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

`ivi`

,
`cent_network.vis`

Other centrality functions:
`betweenness()`

,
`collective.influence()`

,
`degree()`

,
`h_index()`

,
`lh_index()`

,
`neighborhood.connectivity()`

,
`sirir()`

MyData <- coexpression.data My_graph <- graph_from_data_frame(MyData) GraphVertices <- V(My_graph) cr <- clusterRank(graph = My_graph, vids = GraphVertices, directed = FALSE, loops = TRUE)

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