Thanks Theo,

You're right, perhaps I've oversimplified. 

I had to read the prerequisit a paper first:
0007235 v2
"Random graphs with arbitrary degree distributions and
their applications" 
M. E. J. Newman, S. H. Strogatz, and D. J. Watts

I don't think you could say that freenet nodes have 
power distributed number of neighbors.  For the most
part they should have  connections about proportional
to bandwidth.  I suppose you could try to use "Graphs
with arbitrary specified degree distribution" section
from Newman's paper.

I think there is still one more problem here:   All
these networks assume that where the connections go
from one node is random relative to were they come
from.  This is absolutely not true in any of the DHT
networks and shouldn't be true for freenet either. 
I'm pretty sure if it were true routing couldn't be
O(log(N)/log(m)) but something like O(N/m).  In short,
freenet nodes connect more to others with the same
specialization, which makes freenet non-random in this
sense.

I'm still tring to rap my head around those papers. 
I'd like to keep it simple and stupid for a moment. 
Say I transmitted out say t random KSK inserts and had
k nodes logging any of these KSKs (and their HTLs). 
If they caught s of the inserts, would I have a pretty
good idea of what N is?

s ~= HTL*t*k/N
N ~= HTL*t*k/s

I know if the nodes are too "close" to where the
insertions are happening it might screw things up. 
But otherwise it seems like it'd work, if the sampling
nodes bare an average load.  

Sample numbers:
k =  5 test nodes
t = 10000 test messages
HTL = 20
Say N was 10000
s ~= 100 right?

It'd probably be a very rough estimate, but maybe
something that we could use, right?


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