Description Usage Arguments Details Value Author(s) References Examples
View source: R/MeasureNetworkInformation.R
Given an igraph
network, repeatedly perturb the
graph and take some measure of the network to see how
much the measure varies, then return a measure that
increases as the precision of the function values
increases.
1 2 3 | MeasureNetworkInformation(g, FUN = betweenness,
remove.share = 0.2, sample.size = 100,
progress.bar = FALSE)
|
g |
igraph, graph to measure |
FUN |
function, a function that takes an igraph and returns a value for each node in the network. |
remove.share |
numeric, fraction of the edges that are removed randomly when perturbing the network. |
sample.size |
numeric, number of perturbed graphs to generate |
progress.bar |
logical, if TRUE then a progress bar is shown. |
This function can vary tremendously based on the network measure being considered and the other parameters. It is only recommended that this be used for comparing the informativeness of two networks on the same set of nodes, keeping all the parameters the same.
Here information is measured as 1 / mean across and perturbed graphs nodes of the relative error of a network node measure.
Specifically, FUN
is applied to the graph g
to generate reference values. Some sample.size
copies of the igraph are generated. For each,
round(remove.share * n.edges)
randomly selected
edges are dropped to generate a perturbed graph. For
each perturbed graph FUN
is applied, generating a
value for each node in the network. For each node the
relative error
abs( (measure of perturbed g - measure of g) / measure of g )
is calculated, then the mean of these across nodes and perturbed graphs is calculated, generating a mean relative error for the network. This value is reciprocated to get a measure of precision.
This measure appears to be very sensitive to the choice
of FUN
.
numeric, mean precision of the measure FUN
across
the network
Stephen R. Haptonstahl srh@haptonstahl.org
https://github.com/shaptonstahl/dils
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | g.rand <- random.graph.game(100, 5/100)
m.rand <- MeasureNetworkInformation(g.rand)
m.rand
pf <- matrix( c(.8, .2, .3, .7), nr=2)
g.pref <- preference.game(100, 2, pref.matrix=pf)
m.pref <- MeasureNetworkInformation(g.pref)
m.pref
m.pref / m.rand # Relative informativeness of this preference graph
# to this random graph with respect to betweenness
## Not run:
prob.of.link <- c(1:50)/100
mnis <- sapply(prob.of.link, function(p)
MeasureNetworkInformation(random.graph.game(100, p)))
plot(prob.of.link, mnis,
type="l",
main="Network Information of random graphs",
xlab="probability of link formation",
ylab="information")
mtext("with respect to betweenness measure", line=0.5)
## End(Not run)
|
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