Leading Eigenvector Community Detection — community_leading

Detects communities using the leading eigenvector of the modularity matrix. Hierarchical divisive algorithm.

Usage

community_leading_eigenvector(
  x,
  weights = NULL,
  steps = -1,
  start = NULL,
  options = igraph::arpack_defaults(),
  callback = NULL,
  extra = NULL,
  env = parent.frame(),
  ...
)

com_le(
  x,
  weights = NULL,
  steps = -1,
  start = NULL,
  options = igraph::arpack_defaults(),
  callback = NULL,
  extra = NULL,
  env = parent.frame(),
  ...
)

Arguments

x: Network input
weights: Edge weights. NULL uses network weights, NA for unweighted.
steps: Maximum number of splits. Default -1 (until modularity decreases).
start: Starting community structure (membership vector).
options: ARPACK options list. Default uses igraph::arpack_defaults().
callback: Optional callback function called after each split.
extra: Extra argument passed to callback.
env: Environment for callback evaluation.
...: Additional arguments passed to to_igraph

Value

A cograph_communities object

A cograph_communities object. See detect_communities.

References

Newman, M.E.J. (2006). Finding community structure using the eigenvectors of matrices. Physical Review E, 74, 036104.

Examples

g <- igraph::make_graph("Zachary")
comm <- community_leading_eigenvector(g)
membership(comm)
#>  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 
#>  1  3  3  3  1  1  1  3  2  2  1  1  3  3  2  2  1  3  2  3  2  3  2  4  4  4 
#> 27 28 29 30 31 32 33 34 
#>  2  4  4  2  2  4  2  2 
net <- as_cograph(matrix(runif(25), 5, 5))
com_le(net)
#> Community structure (leading_eigenvector)
#>   Nodes: 5  | Communities: 3  | Modularity: 0.0682 
#>   Sizes: 2, 2, 1 
#> 
#>  node community
#>     1         1
#>     2         2
#>     3         2
#>     4         1
#>     5         3