Equivalence clustering algorithms

These functions combine an appropriate _census() function together with methods for calculating the hierarchical clusters provided by a certain distance calculation.

A plot() method exists for investigating the dendrogram of the hierarchical cluster and showing the returned cluster assignment.

node_equivalence(
  object,
  census,
  k = c("silhouette", "elbow", "strict"),
  cluster = c("hierarchical", "concor"),
  distance = c("euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski"),
  range = 8L
)

node_structural_equivalence(
  object,
  k = c("silhouette", "elbow", "strict"),
  cluster = c("hierarchical", "concor"),
  distance = c("euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski"),
  range = 8L
)

node_regular_equivalence(
  object,
  k = c("silhouette", "elbow", "strict"),
  cluster = c("hierarchical", "concor"),
  distance = c("euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski"),
  range = 8L
)

node_automorphic_equivalence(
  object,
  k = c("silhouette", "elbow", "strict"),
  cluster = c("hierarchical", "concor"),
  distance = c("euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski"),
  range = 8L
)

Source

https://github.com/aslez/concoR

Arguments

object

An object of a migraph-consistent class:

• matrix (adjacency or incidence) from {base} R

• edgelist, a data frame from {base} R or tibble from {tibble}

• igraph, from the {igraph} package

• network, from the {network} package

• tbl_graph, from the {tidygraph} package

census

A matrix returned by a node_*_census() function.

k

Typically a character string indicating which method should be used to select the number of clusters to return. By default "silhouette", other options include "elbow" and "strict". "strict" returns classes with members only when strictly equivalent. "silhouette" and "elbow" select classes based on the distance between clusters or between nodes within a cluster. Fewer, identifiable letters, e.g. "e" for elbow, is sufficient. Alternatively, if k is passed an integer, e.g. k = 3, then all selection routines are skipped in favour of this number of clusters.

cluster

Character string indicating whether clusters should be clustered hierarchically ("hierarchical") or through convergence of correlations ("concor"). Fewer, identifiable letters, e.g. "c" for CONCOR, is sufficient.

distance

Character string indicating which distance metric to pass on to stats::dist. By default "euclidean", but other options include "maximum", "manhattan", "canberra", "binary", and "minkowski". Fewer, identifiable letters, e.g. "e" for Euclidean, is sufficient.

range

Integer indicating the maximum number of (k) clusters to evaluate. Ignored when k = "strict" or a discrete number is given for k.

Functions

• node_equivalence(): Returns nodes' membership in according to their equivalence with respective to some census/class

• node_structural_equivalence(): Returns nodes' membership in structurally equivalent classes

• node_regular_equivalence(): Returns nodes' membership in regularly equivalent classes

• node_automorphic_equivalence(): Returns nodes' membership in automorphically equivalent classes

References

Thorndike, Robert L. 1953. "Who Belongs in the Family?". Psychometrika, 18(4): 267–76. doi:10.1007/BF02289263 .

Rousseeuw, Peter J. 1987. “Silhouettes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis.” Journal of Computational and Applied Mathematics, 20: 53–65. doi:10.1016/0377-0427(87)90125-7 .

See also

Other memberships: community, components(), core-periphery

Examples

# \donttest{
(nse <- node_structural_equivalence(mpn_elite_usa_advice))
#> 1 
#>     
#>    Albright, Curry, Fukuyama, Gray, Wald
#>  2 
#>     
#>    Argyros, Armitage, Greenberg, Hills, Kissinger, Peterson, Scowcroft, West, Zakheim
#>  3 
#>    ACUS, CNI, CSIS, FPRI, GMFUS, HOOVER 
#>    
#>  4 
#>    AEI, EWI, MI 
#>    
#>  5 
#>    ASPEN, CFR, CNAS, IIE 
#>    
#>  6 
#>    CATO, CGD, RF, RFF 
#>    
#>  7 
#>    IAD, NAF, RAND 
#>    
plot(nse)

# }
# \donttest{
(nre <- node_regular_equivalence(mpn_elite_usa_advice,
  cluster = "concor"))
#> 1 
#>     
#>    Albright, Curry, Fukuyama, Wald, CFR, NAF, IIE, Greenberg
#>  2 
#>    Scowcroft 
#>    Argyros, Peterson, Armitage, West, Zakheim, Kissinger
#>  3 
#>    AEI, ASPEN, EWI, FPRI, GMFUS, HOOVER, CATO, CGD, RF, RAND, RFF 
#>    
#>  4 
#>    CNAS, MI, IAD 
#>    
#>  5 
#>    Gray 
#>    
#>  6 
#>    Hills 
#>    
#>  7 
#>    ACUS, CNI 
#>    
#>  8 
#>    CSIS 
#>    
plot(nre)

# }
# \donttest{
(nae <- node_automorphic_equivalence(mpn_elite_usa_advice,
  k = "elbow"))
#> 1 
#>    ASPEN, CFR, CNI, CSIS, HOOVER 
#>    Albright, Argyros, Hills, Peterson
#>  2 
#>    CNAS, FPRI, GMFUS 
#>    Armitage, Kissinger, Scowcroft, West, Zakheim
#>  3 
#>    AEI, IAD, IIE, NAF, RAND 
#>    Curry, Fukuyama, Greenberg
#>  4 
#>    ACUS, CATO, CGD, EWI, MI, RF, RFF 
#>    Gray, Wald
plot(nae)

# }