context("assortativity")
test_that("assortativity works", {
library(igraph)
g <- read_graph(f <- gzfile("celegansneural.gml.gz"), format="gml")
assR <- function(graph) {
indeg <- degree(graph, mode="in")
outdeg <- degree(graph, mode="out")
el <- as_edgelist(graph, names=FALSE)
J <- outdeg[el[,1]]-1
K <- indeg[el[,2]]-1
num <- sum(J*K) - sum(J)*sum(K)/ecount(graph)
den1 <- sum(J*J) - sum(J)^2/ecount(graph)
den2 <- sum(K*K) - sum(K)^2/ecount(graph)
num / sqrt(den1) / sqrt(den2)
}
asd <- assortativity_degree(g)
as <- assortativity(g, degree(g, mode="out"), degree(g, mode="in"))
as2 <- assR(g)
expect_that(asd, equals(as))
expect_that(asd, equals(as2))
asu <- assortativity_degree(simplify(as.undirected(g, mode="collapse")))
expect_that(asu, equals(-0.16319921031570466807))
p <- read_graph(f <- gzfile("power.gml.gz"), format="gml")
p.asd <- assortativity_degree(p)
p.as <- assortativity(p, degree(p))
p.as2 <- assR(as.directed(p, mode="mutual"))
expect_that(p.asd, equals(p.as))
expect_that(p.asd, equals(p.as2))
})
test_that("nominal assortativity works", {
library(igraph)
o <- read_graph(f <- gzfile("football.gml.gz"), format="gml")
o <- simplify(o)
an <- assortativity_nominal(o, V(o)$value+1)
el <- as_edgelist(o, names=FALSE)
etm <- matrix(0, nrow=max(V(o)$value)+1, ncol=max(V(o)$value)+1)
for (e in 1:nrow(el)) {
t1 <- V(o)$value[ el[e,1] ]+1
t2 <- V(o)$value[ el[e,2] ]+1
etm[t1, t2] <- etm[t1, t2] + 1
etm[t2, t1] <- etm[t2, t1] + 1
}
etm <- etm/sum(etm)
an2 <- ( sum(diag(etm))-sum(etm %*% etm) ) / ( 1-sum(etm %*% etm) )
expect_that(an, equals(an2))
})
Generated by dwww version 1.15 on Sun Jun 16 12:34:28 CEST 2024.