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\myprogram{{model\_layer\_growth.py}}
{Layer growth with preferential activation model.}
{$<$layer\_N\_file$>$ $<$N$>$ $<$M0$>$ $<$A$>$ [RND]}
\mydescription{This is the model of layer growth with preferential
node activation. In this model an entire new layer arrives
at time $t$ and a number of nodes $N_t$ is activated ($N\_t$
is equal to the number of nodes active on that layer in the
reference multiplex). Then, each node $i$ of the new layer
is activated with a probability:
\begin{equation*}
P_i(t) \propto A + B_i(t)
\end{equation*}
where $B_i(t)$ is the activity of node $i$ at time $t$
(i.e., the number of layers in which node $i$ is active at
time $t$) while $A>0$ is an intrinsic attractiveness.
The file \textit{layer\_N\_file} reports on the n-th line
the number of active nodes on the n-th layer.
The parameter \textit{N} is the number of nodes in the
multiplex, \textit{M0} is the number of layers in the
initial network, \textit{A} is the value of
node attractiveness.
If the user specifies \texttt{RND} as the last parameter,
the sequence of layers is }
\myreturn{The program prints on \texttt{stdout} a node-layer list of lines in the
format:
\hspace{0.5cm} \textit{node\_i layer\_i}
where \textit{node\_i} is the ID of a node and \textit{layre\_i} is
the ID of a layer. This list indicates which nodes are active in
which layer. For instance, the line:
\hspace{0.5cm} \textit{24 3}
indicates that the node with ID \textit{24} is active on
layer \textit{3}.
}
\myreference{\refcorrelations}
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