s_coreness {scoredec} | R Documentation |
s-core community decomposition
Description
s-core community decomposition
Usage
s_coreness(g = NULL, W = NULL, mode = "all")
Arguments
g |
|
W |
|
mode |
|
Details
s-core community decomposition implementation. Only one of g
or W
must be provided.
While the source code is not as clear as the one at brainGraph::s_core
,
it is very speed and memory efficient. In case that the adjacency matrix
W
is provided instead of the graph g
is provided, then this
function is very speed and memory efficient.
Note that in cases that the adjacency matrix W
is known to be symmetric
(checked, for example, with base::isSymmetric
or
Rfast::is.symmetric
), then mode = "in"
and mode = "out"
will produce the same result more efficiently. For efficiency reasons not
checking it is chosen, but user should do it.
Value
Integer vector with s-coreness attribute to each vertex.
References
Eidsaa, M. and Almaas, E. (2013) ‘s-core network decomposition: A generalization of k-core analysis to weighted networks’, Phys. Rev. E., American Physical Society, 88, 062819. doi:10.1103/PhysRevE.88.062819.
Watson, C.G. (2024). brainGraph: Graph Theory Analysis of Brain MRI Data. R package version 3.1.0. doi:10.32614/CRAN.package.brainGraph.
Examples
set.seed(42)
# Create a dummy symmetric adjacency matrix
n <- 5
W <- matrix(runif(n^2),n)
W[lower.tri(W)] = t(W)[lower.tri(W)]
diag(W) <- 0
print(scoredec::s_coreness(g = NULL, W = W, mode = "all"))
## [1] 3 1 2 4 4
base::isSymmetric(W)
## [1] TRUE
all.equal(scoredec::s_coreness(g = NULL, W = W, mode = "all"),
scoredec::s_coreness(g = NULL, W = W, mode = "in"))
## [1] TRUE
# Create a dummy undirected graph
g <- igraph::graph_from_adjacency_matrix(adjmatrix = W,
mode = "undirected",
weighted = TRUE)
print(scoredec::s_coreness(g = g, W = NULL, mode = "all"))
## [1] 3 1 2 4 4