I have a network which I need to fit with power law distribution and exponential distribution and compare them, choosing the better fit.
I have degree distribution data retrived using igraph package degree.distribution function:
degree.distribution (data, mode = "all", cumulative = FALSE)
which returns results vector eg
0.000000000 0.289772727 0.278409091 0.159090909 0.204545455 0.051136364 0.005681818 0.011363636
I tried fitting it using igraph's power.law.fit as below:
power.law.fit(deg.dist, impelementation = "plfit")
$continuous
[1] TRUE
$alpha
[1] 1.493625
$xmin
[1] 0.008
$logLik
[1] 10.32315
$KS.stat
[1] 0.1248314
$KS.p
[1] 0.9996425
my question is: I need to fit the data to an exponential distribution as well and compare the results, so I'm looking for a way to create the fits that returns comparable parameters. If there's better ways to find a power law fit, I'd be happy to try them.
Thank you
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