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MCN

Learning to solve the Multilevel Critical Node (MCN) Problem. Implementation of the paper Curriculum learning for multilevel budgeted combinatorial problems .

Setup

Install the package, e.g as following

python -m pip install git+https://github.com/AdelNabli/MCN/

Requirements

Usage

There are 3 main tasks supported:

  • Train a neural network to produce a pool of 'expert nets' in order to solve the MCN problem on a distribution of instances
  • Solve an instance of the MCN problem, either using an exact method or heuristically with the trained experts
  • Evaluate the performances of the trained experts on a test set of exactly solved MCN instances

An example of how to perform each of these tasks is given in the Notebook

Citation

@article{nabli2020curriculum,
    title={Curriculum learning for multilevel budgeted combinatorial problems},
    author={Adel Nabli and Margarida Carvalho},
    year={2020},
    journal={arXiv preprint arXiv:2007.03151}
}

Acknowledgement

The MCN problem was introduced in A. Baggio, M. Carvalho, A. Lodi, A. Tramontani, "Multilevel Approaches for the Critical Node Problem", 2018. The exact method used here is a simple implementation of the algorithm described in this paper, with a few additions. Our implementation is based on the original code found in the following Github repository: mxmmargarida/Critical-Node-Problem

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Learning to solve the Multilevel Critical Node Problem. Code for the paper https://arxiv.org/pdf/2007.03151.pdf

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