We adopt the following notation: N and L indicate respectively the total number of
nodes and links of the network, Air indicates the generic element of the adjacency
matrix A of the network, ki
indicates the degree of node i and hki indicates the
average degree of the network.
At time t = 1 the network is formed by a n0 = 6 nodes m0 = 6 links.
At every time step t > 1 the network evolves according to the following rules:
- A link (r,s) between a node r and a node s is chosen randomly with uniform
probability
π(r,s) = Ar,s/L
and is removed from the network.
- A single new node joins the network and is connected to the rest of the network
by m links with m fixed to a time-independent integer constant satisfying
2 < m ≤ 6. Each of these new links connects the new node to a generic node j
chosen with probability
Πj =kj/(k)N
.a) Evaluate Π˜
i(t) indicating the expected increase in the number of links of node i
at any given time t and show that it follows the preferential attachment rule.
"\\Pi_i=\\sum_{r=1}^N \\pi(i,r)=\\sum _{r=1}^N\\dfrac{A_{ir}}{L}"
As we know, "L=\\dfrac{1}{2}\\sum_{j=1}^NK_j"
and "k_i=\\sum_{r=1}^NA_{ir}"
So, "\\Pi_i=\\sum_{r=1}^N\\dfrac{K_i}{\\frac{1}{2}\\sum_{j=1}^Nk_j}"
"\\Pi_i=\\dfrac{2k_i}{\\sum_{j=1}^NK_j}"
So we have "\\Pi_1,\\Pi_2,\\Pi_3.." .
According to question-
"\\dfrac{dk_i(t)}{dt}=\\Pi_i=\\dfrac{2k_i}{\\sum_{j=1}^NK_j}"
We limit "t\\ge 1" , we have "\\sum _jK_j =2L \\sim ut"
Initilly "K_i(t_i)=6"
So , "k_i(t_i)=6(\\dfrac{t}{t_1})^{0.5}"
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