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
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)
igraph::membership(comm)
#> [1] 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 2 4 4 2 2 4 2 2
net <- as_cograph(matrix(runif(25), 5, 5))
com_le(net)
#> Community structure (leading_eigenvector)
#> Number of communities: 2
#> Modularity: 0.0413
#> Community sizes: 2, 3
#> Nodes: 5