It still depends on the network that you are using for analysis.
Assuming the paper uses the same dataset as the link Sean provided the
giant cluster they are analyzing is only 8.3% of IP nodes in their
sample. It takes the removal of 25% when only looking at that small
densely connected section, it says nothing about what will happen to the
other 91.7% of nodes. Considering that 55% of the remaining nodes are
trees, they will be saying "Houston we have a problem" well before 25%.
Whether or not it matters that they have a problem in an entirely
different question. I've probably kicked this dead horse enough already
"Sean" == <sgorman1@gmu.edu> writes:
> it says nothing about what will happen to the other 91.7% of
> nodes. Considering that 55% of the remaining nodes are
> trees, they will be saying "Houston we have a problem" well
> before 25%.
The supposition would be that the remaining nodes are evenly
distributed around the core so the percentage of nodes outside of the
core without connectivity should be roughly the same as the percentage
of nodes removed from the core. At least until the core goes
non-linear...
>> It would be interesting to see what outdegree looks like as a
>> function of rank -- in the paper they give only the maximum and
>> average (geo. mean) outdegrees. Is there also a critical point
>> 25% of the way through the ranking? Probably not or one would
>> expect they'd have mentioned it...
It turns out that this is buried in one of the graphs (fig. 6) and
does not appear to have any special properties 25% of the way through.
It does have an inflection point around the 1000th node or so (2.5%).
-w