NAME
model_layer_growth.py - Layer growth with preferential activation model.
SYNOPSYS
model_layer_growth.py <layer_N_file> <N> <M0> <A> [RND]
DESCRIPTION
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 Nt 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:
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where Bi(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 layer_N_file reports on the n-th line the number of active nodes on the n-th layer.
The parameter N is the number of nodes in the multiplex, M0 is the number of layers in the initial network, A is the value of node attractiveness.
If the user specifies RND as the last parameter, the sequence of layers is
OUTPUT
The program prints on stdout a node-layer list of lines in the format:
node_i layer_i
where node_i is the ID of a node and layre_i is the ID of a layer. This list indicates which nodes are active in which layer. For instance, the line:
24 3
indicates that the node with ID 24 is active on layer 3.
REFERENCE
V. Nicosia, V. Latora, “Measuring and modeling correlations in multiplex networks”, Phys. Rev. E 92, 032805 (2015).
Link to paper: http://journals.aps.org/pre/abstract/10.1103/PhysRevE.92.032805