Hi,
A combined reply to Paul Johnston who wrote:
> I'm not doing that kind of thing right now, but i'd be very
interested
> to hear more on how you implement it.
and Gulyas Laszlo who wrote:
> The most famous algorithm to create scale-free networks (rather:
networks
> with power law degree distribution) is the Preferential
Attachment model
> of Albert and Barabasi. Try Google, you'll be successful.
My choice of wanting to use a scale-free network has been
influenced by the book "Linked" by Albert-Laszlo Barabasi, who is
Professor of physics at the University of Notre Dame. (Are the
Laszlo in Gulyas Laszlo and Albert-Laszlo Barabasi linked?). I
realise that there are different algorithms to generate a scale
free model, my current requirement is just to generate a network
with static links, following the power law distribution, so I think
a simpler algorithm than the Preferential Attachment model will do.
I haven't programmed it in SWARM as yet, but having played with the
following in Excel seemed to give a reasonably good (for my
application at least) power law distribution:
If you have a list of people, and now you want to generate a number
"numLinks", for each person in your list, that is the number of
other people in the list influencing her, then use the following:
numLinks = a * exp [ b/(c + x)]
with x = d + e*f
with f = randomly distributed number between 0 and 1
a,b,c,d & e are adjustable coefficients giving different power law
distributions.
A set of values that I have tried is:
a = 10
b = 20
c = 4
d = 5
e = 195
Pieter Steenekamp
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