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This repository can be used to generate an instance of the Geometric Multiplex Model (GMM). This is an extention of the geometric $\mathbb{S}^1$-model with $L$ layers where the correlations at the level of the hidden coordinates between the layers can be tuned. The full model can be found in Kleineberg2016.
Requirements
# Unix (Linux / MAC OS) g++ -O3 -std=c++11 include_else/hyp2f1.cpp include_me/S1_realization.cpp main.cpp -o GENMULTIPLEX
There are two input files containing S1 parameters and interlayer correlations respectively.
The file parameters.txt contains $L$ lines, one for each layer. It must have the following structure:
[gamma layer 1] [beta layer 1] [average degree layer 1] [random seed layer 1] [gamma layer 2] [beta layer 2] [average degree layer 2] [random seed layer 2] ... [gamma layer L] [beta layer L] [average degree layer L] [random seed layer L]
Note: This version of the GMM supports two degree distributions:
The file correlations.txt contains $L-1$ lines, one for each consecutive pair of layers. It must have the following structure:
[nu layers 1,2] [g layers 1,2] [nu layers 2,3] [g layers 2,3] ... [nu layers L-1,L] [g layers L-1,L]
Running GENMULTIPLEX can be done with or without flags
# Command line with flags ./GENMULTIPLEX -N <network size> -pf <parameter file name> -cf <correlation file name> # Command line without flags ./GENMULTIPLEX <network size> <parameter file name> <correlation file name>
The program outputs the multiplex using a set of files. For each layer i, a hidden coordinate file layer<i>.coord is generated which contains two columns. The first contains the angular coordinates and the second the hidden degrees. Additionally, for each layer i an edge file layer<i>.edge is created.
Please cite:
Hidden geometric correlations in real multiplex networks
Kaj Kolja Kleineberg, Marián Boguñá, M. Ángeles Serrano and Fragkiskos Papadopoulos
Nature Physics 12, 1076-1081 (2016)
Full text | arXiv
Multiplexity amplifies geometry in networks
Jasper van der Kolk, Dmitri Krioukov, Marián Boguñá and M. Ángeles Serrano
arXiv preprint arXiv:2505.17688
arXiv
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