CC: Clustering Coefficient
In SemNeT: Methods and Measures for Semantic Network Analysis
CC R Documentation
Clustering Coefficient
Description
Computes global clustering coefficient CC
Usage
CC(A, weighted = FALSE)
Arguments
A
An adjacency matrix of network data
weighted
Is the network weighted?
Defaults to FALSE.
Set to TRUE for weighted measures of CC and CCi
Value
Returns the network's CC
Author(s)
Alexander Christensen <alexpaulchristensen@gmail.com>
References
Rubinov, M., & Sporns, O. (2010).
Complex network measures of brain connectivity: Uses and interpretations.
NeuroImage, 52, 1059-1069.
Examples
# Pearson's correlation only for CRAN checks
A <- TMFG(similarity(sim.fluency(100), method = "cor"))
# Unweighted
cc <- CC(A)
SemNeT documentation built on Aug. 12, 2023, 5:06 p.m.
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| CC | R Documentation |
Clustering Coefficient
Description
Computes global clustering coefficient CC
Usage
CC(A, weighted = FALSE)
Arguments
A |
An adjacency matrix of network data |
weighted |
Is the network weighted?
Defaults to |
Value
Returns the network's CC
Author(s)
Alexander Christensen <alexpaulchristensen@gmail.com>
References
Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52, 1059-1069.
Examples
# Pearson's correlation only for CRAN checks
A <- TMFG(similarity(sim.fluency(100), method = "cor"))
# Unweighted
cc <- CC(A)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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