
From:  Daniele Capocefalo 
Subject:  Re: [igraph] Understanding Barabasi Function and how to simulate a scale free() 
Date:  Thu, 4 May 2017 15:16:16 +0200 
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Dear Tamas, Thanks for your detailed reply. I have one doubt regarding this part:
I have found the alpha of my networks using power_law_fit(),
then generated Random scalefree network that has only one large
components using Static_Power_Law(). For some of my networks, when
recomputing the power_law_fit on the simulated scale free
networks, the alpha varies consistenyl. As of an example, I'll
post here the results obtained using a network of 84 nodes and 221
edges and an alpha estimated on 2.614071596
Example #1 a= g.Static_Power_Law(n=84,m=221,exponent_out = 2.61407159636724, loop = False, multiple = False) fit = st.power_law_fit(a.degree()) fit.alpha 6.616842211661753 Example #2 b= g.Static_Power_Law(n=84,m=221,exponent_out = 2.61407159636724, loops= False, multiple = False) fit = st.power_law_fit(b.degree()) fit.alpha 4.455056295669706 Example #3 c= g.Static_Power_Law(n=84,m=221,exponent_out = 2.61407159636724,
loops= False, multiple = False)
Is this to be expected? I have proven this in several simulations using different networks and parameters.
Thanks a lot!
cheers,
Daniele
On 21/04/17 11:38, Tamas Nepusz wrote:
 ——— Daniele Capocefalo Bioinformatics unit Casa Sollievo della Sofferenza  Mendel Viale Regina Margherita 261  00198 Roma IT Tel: +39 06 44160526  Fax: +39 06 44160548 Email: address@hidden Web page: http://www.cssmendel.it/ Web page: http://bioinformatics.cssmendel.it 
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