2.1.1.0 model_layer

From a86962cbfd0321387c920a04188512d0de2f3036 Mon Sep 17 00:00:00 2001 From: KatolaZ Date: Mon, 19 Oct 2015 16:30:12 +0100 Subject: First commit of MAMMULT documentation --- doc/html/mammult_docsu40.html | 138 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 138 insertions(+) create mode 100644 doc/html/mammult_docsu40.html (limited to 'doc/html/mammult_docsu40.html') diff --git a/doc/html/mammult_docsu40.html b/doc/html/mammult_docsu40.html new file mode 100644 index 0000000..5edece7 --- /dev/null +++ b/doc/html/mammult_docsu40.html @@ -0,0 +1,138 @@ + + +2.1.1.0 model_layer_growth.py + + + + + + + + +

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model_layer_growth.py

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 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: +
+ $Pi(t) ∝ A + Bi (t) +$
+

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 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 + + + +

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