Optimizing network robustness via Krylov subspaces

Abstract

We consider the problem of attaining either the maximal increase or reduction of the robustness of a complex network by means of a bounded modification of a subset of the edge weights. We propose two novel strategies combining Krylov subspace approximations with a greedy scheme and with the limited-memory BFGS. The paper discuss the computational and modeling aspects of our methodology and illustrates the various optimization problems on networks that can be addressed within the proposed framework. Finally, in the numerical experiments we compare the performances of our algorithms with state-of-the-art techniques on synthetic and real-world networks.

Please cite this work as:

@article{massei2023optimizing,
  title={Optimizing network robustness via {K}rylov subspaces
},
  author={Massei, Stefano and Tudisco, Francesco},
  journal={ESAIM: Mathematical Modelling and Numerical Analysis},
  year={2024}
}

Keywords: matrix functions graph mining networks Krylov subspace