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ALCOREM-0.1

This is ALCOREM(ALgorithmic COmplexity and REducibility of Multiplex networks), a C implementation of the algorithm for computing the algorithmic complexity and reducibility of a multiplex networks.

In the folders there are two different C codes, which respectively implement the algorithms to compute the algorithmic complexity of a multiplex network and the one for reducing the multiplex network. The algorithms are based on the procedure described in the paper:

A.Santoro, V. Nicosia, Algorithmic Complexity of Multiplex Networks, Phys. Rev. X 10, 021069 (2020) https://doi.org/10.1103/PhysRevX.10.021069

The code has been tested on OSX and Linux distributions using gcc/clang/icc compilers.

Dependencies

The algorithm makes use of the following libraries:

Therefore, before using the code, make sure you have installed it. Otherwise, you can install easily as:

On Debian based systems:

sudo apt-get install libgmp3-dev
sudo apt-get install libomp-dev
sudo apt-get install zlib1g-dev

On OSX through brew:

brew install gmp
brew install zlib
brew install libomp

Sample bash scripts to launch the code with different options can be found in the "Example/" directory, where you will also find a sample real-world data set. This is the 13 layers London tube, originally provided in:

M. De Domenico, A. Solé-Ribalta, S. Gómez, and A. Arenas, Navigability of interconnected networks under random failures. PNAS 111, 8351-8356 (2014)

Please consider citing this paper if you use this data set in a scientific work.

Another synthetic example is also reported in the directory "Example/". This include the synthetic multiplex network made of 10 ER random graphs reported in Fig 3c of the paper, i.e. five pairs of identical layers.

License

This project is licensed under the GNU GPLv3 license - see the LICENSE file for details


Python Package

My plan is to provide a python wrapper to better integrate the C code with python scripts in the near future.

About

Code associated with the paper 'Algorithmic complexity of multiplex networks' (https://journals.aps.org/prx/abstract/10.1103/PhysRevX.10.021069)

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