context("cluster_leading_eigen")
test_that("cluster_leading_eigen works", {
library(igraph)
## Check-test
f <- function(membership, community, value, vector, multiplier, extra) {
M <- sapply(1:length(vector), function(x) {
v <- rep(0, length(vector))
v[x] <- 1
multiplier(v)
})
ev <- eigen(M)
ret <- 0
expect_that(ev$values[1], equals(value))
if (sign(ev$vectors[1,1]) != sign(vector[1])) {
ev$vectors <- -ev$vectors
}
expect_that(ev$vectors[,1], equals(vector))
0
}
g <- make_graph("Zachary")
lc <- cluster_leading_eigen(g, callback=f)
expect_that(lc$modularity, equals(modularity(g, lc$membership)))
expect_that(as.vector(membership(lc)),
equals(c(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)))
expect_that(length(lc), equals(4))
expect_that(sizes(lc),
equals(structure(c(7L, 12L, 9L, 6L), .Dim = 4L, .Dimnames =
structure(list(`Community sizes` =
c("1", "2", "3", "4")),
.Names = "Community sizes"),
class = "table")))
## Check that the modularity matrix is correct
f <- function(membership, community, value, vector, multiplier, extra) {
M <- sapply(1:length(vector), function(x) {
v <- rep(0, length(vector))
v[x] <- 1
multiplier(v)
})
myc <- membership==community
B <- A[myc,myc] - (deg[myc] %*% t(deg[myc]))/2/ec
BG <- B-diag(rowSums(B))
expect_that(M, equals(BG))
0
}
g <- make_graph("Zachary")
A <- as_adj(g, sparse=FALSE)
ec <- ecount(g)
deg <- degree(g)
lc <- cluster_leading_eigen(g, callback=f)
## Stress-test
for (i in 1:100) {
g <- sample_gnm(20, sample(5:40, 1))
lec1 <- cluster_leading_eigen(g)
lec2 <- cluster_leading_eigen(g)
expect_that(as.vector(membership(lec1)),
equals(as.vector(membership(lec2))))
}
})
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