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PIF-MN: A Framework for Investigating Perturbations and Their Effects in Multilayer Networks

Gianluca Bonifazi et al.

International Journal of Information Technology and Decision Making2026https://doi.org/10.1142/s0219622026500306article
ABDC C
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0.50

What the paper says

In this paper, we propose Perturbation Investigation Framework for Multilayer Networks (PIF-MN), a framework for analyzing perturbations on multilayer networks. A perturbation is a generic action, directed toward a target, whose effect is to alter the state of the multilayer network, i.e., the set of its nodes, intralayer and interlayer arcs. The study of perturbations in multilayer networks is important because these networks are increasingly used to model heterogeneous and often critical scenarios where any perturbation can have dramatic cascading effects. PIF-MN considers a wide range of perturbation strategies, both single and combined. It also proposes several structural and connectivity measures allowing for the evaluation of the effects of each type of perturbation on a multilayer network and the resilience of this network to them. After introducing PIF-MN, we describe a set of experiments for testing it on both synthetic multilayer networks, built by applying two distinct random models, namely Erdős–Rényi and Barabási–Albert, and a real-world multilayer network. The results of these experiments highlight the robustness of multilayer networks against potential perturbations.

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https://doi.org/https://doi.org/10.1142/s0219622026500306

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@article{gianluca2026,
  title        = {{PIF-MN: A Framework for Investigating Perturbations and Their Effects in Multilayer Networks}},
  author       = {Gianluca Bonifazi et al.},
  journal      = {International Journal of Information Technology and Decision Making},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1142/s0219622026500306},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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