Simone, i don't know your graphs, but yes, i guess that you should
only fit the tail, at least this works for the "standard" BA model:
g <- barabasi.game(100000)
res <- power.law.fit(degree(g), xmin=20)
res
Call:
mle(minuslogl = mlogl, start = list(alpha = start))
Coefficients:
alpha
2.945498
power.law.fit seems to work fine in general:
prob <- (1:10000)^-2.5
data <- sample(1:10000, 1000, replace=TRUE, prob=prob)
power.law.fit(data)
Call:
mle(minuslogl = mlogl, start = list(alpha = start))
Coefficients:
alpha
2.516897
You might want to plot the degree distribution and the fit.
Also, see ?"mle-class" for more information about the actual fit.
G.
On Mon, Sep 03, 2007 at 05:51:18PM +0200, Simone Gabbriellini wrote:
dear list,
I've used the function power.law.fit in this way:
c<-degree(myGraph);
pwl<-power.law.fit(c+1);
for a list of 25 graphs. Now I have values in the range 1,296295
to 1,805046.
I see that the value usually expected goes between 2 to 3 for real
networks...
can you help me understand my results? maybe I have to specify the
xmin value?
being under 2 means something particular?
thanks a lot,
simone
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Csardi Gabor <address@hidden> MTA RMKI, ELTE TTK
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