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Giant clusters
We have seen that increasing p has the effect of establishing a larger number of links
and also that of creating clusters, the triangles found in Figure 6.7 being, in fact, a kind
of lowest-order cluster. Is it possible to find a critical value of p , much smaller than
1, that allows a walker to move freely from one border to the opposite border of the
network? In Figure 6.8 we sketch a giant cluster making it possible for the walker to
realize a complete North-South or East-West trip. What is the critical value of p that
we have to assign to the network for this giant cluster to appear?
Let us denote by s the fraction of the nodes of the graph that do not belong to the giant
cluster. When the number of nodes is very large, the quantity s can also be interpreted
as the probability that a node randomly chosen from the network does not belong to the
giant component. The fraction S of the graph occupied by the giant cluster is therefore
S
=
1
s
.
(6.68)
Now consider a node not belonging to the giant cluster and assume that the node
has degree k , namely it has k links. If the node we are considering does not belong to
the giant cluster, neither do its k nearest neighbors. In fact, if one of these neighbors
belonged to the giant cluster, then the node under consideration would belong to it as
well. Thus, we obtain
p k s k
s
=
.
(6.69)
k
=
0
In fact, since s is a probability it follows that the probability that the k neighbors do not
belong to the giant cluster is s k . The probability that the node under consideration has k
neighbors and the probability that the neighbors do not belong to the giant component
are independent of one another. This explains the product p k s k . The sum from k
=
0
Figure 6.8. When the probability p reaches a critical value p c = 1 /( N 1 ) a walker can travel from the
Southern to the Northern border and from the Western to the Eastern border. The black links
indicate a giant cluster spanning the world from East to West and from North to South. The gray
links indicate local clusters.
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