| Type: | Package |
| Title: | S-Core Graph Decomposition |
| Version: | 0.1.2 |
| Maintainer: | Christos Adam <econp266@econ.soc.uoc.gr> |
| Description: | S-Core Graph Decomposition algorithm for graphs. This is a method for decomposition of a weighted graph, as proposed by Eidsaa and Almaas (2013) <doi:10.1103/PhysRevE.88.062819>. The high speed and the low memory usage make it suitable for large graphs. |
| License: | GPL-3 |
| LinkingTo: | Rcpp |
| Encoding: | UTF-8 |
| URL: | https://github.com/cadam00/scoredec, https://cadam00.github.io/scoredec/ |
| BugReports: | https://github.com/cadam00/scoredec/issues |
| Imports: | Rcpp (≥ 1.0.12), Rfast, igraph |
| Suggests: | Rfast2, knitr, rmarkdown, testthat (≥ 3.0.0) |
| Config/testthat/edition: | 3 |
| RoxygenNote: | 7.3.2 |
| VignetteBuilder: | knitr, rmarkdown |
| NeedsCompilation: | yes |
| Packaged: | 2024-11-20 06:21:59 UTC; Administrator |
| Author: | Christos Adam [aut, cre] |
| Repository: | CRAN |
| Date/Publication: | 2024-11-20 06:50:01 UTC |
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