calCentrality: Calculate node-/edge-based centralities of a network or a set...

In csclab/RMut: RMut: a collective framework for mutations analysis in network dynamics

Description

Calculate centrality measures for all nodes/edges in a network or in a set of networks.

Usage

calCentrality(networks)

Arguments

networks

A network or a set of networks used for the calculation

Details

This function calculates node-/edge-based centralities of a specific network or a set of networks. For each network, the returned results are stored in a data frame of the network object. The data frame has one column for nodes/edges identifiers, and nine columns contain corresponding values of some node-based centrality measures such as Degree, In-/Out-Degree, Closeness, Betweenness, Stress and Eigenvector, and some edge-based measures like Edge Degree and Edge Betweenness.

Value

The updated network objects including values of centrality measures for each node/edge.

See Also

findFBLs, findFFLs, calSensitivity

Examples

data(amrn)
amrn <- calCentrality(amrn)
print(amrn$nodes)
print(amrn$edges)

csclab/RMut documentation built on May 14, 2019, 12:07 p.m.