3 citations · 3 across the 6 of their papers we have counts for
7 papers
Steering dynamic network centrality via control theory
Fabio Durastante, Beatrice Meini, Luca Saluzzi
Time-evolving networks, or temporal networks, play a crucial role in modeling dynamic interactions across various domains, including biology, social sciences, and information techn…
Advances on the recovery of (perturbed) Cauchy matrices
Paola Boito, Dario Fasino, Beatrice Meini
Given a (possibly approximate) Cauchy matrix, how can we efficiently compute its generators? Expanding on previous work by Liesen and Luce [Linear Algebra Appl. 493 (2016) 261--280…
Nearest Reversible Markov Chains with Sparsity Constraints: An Optimization Approach
Stefano Cipolla, Fabio Durastante, Miryam Gnazzo +1
Reversibility is a key property of Markov chains, central to algorithms such as Metropolis-Hastings and other MCMC methods. Yet many applications yield non-reversible chains, motiv…
Kemeny's constant minimization for reversible Markov chains via structure-preserving perturbations
Fabio Durastante, Miryam Gnazzo, Beatrice Meini
Kemeny's constant measures the efficiency of a Markov chain in traversing its states. We investigate whether structure-preserving perturbations to the transition probabilities of a…
A Riemannian Optimization Approach for Finding the Nearest Reversible Markov Chain
Fabio Durastante, Miryam Gnazzo, Beatrice Meini
We address the algorithmic problem of determining the reversible Markov chain that is closest to a given Markov chain , with an identical stationary distribution. Mor…
Enforcing Katz and PageRank Centrality Measures in Complex Networks
Stefano Cipolla, Fabio Durastante, Beatrice Meini
We investigate the problem of enforcing a desired centrality measure in complex networks, while still keeping the original pattern of the network. Specifically, by representing the…
Stochastic th root approximation of a stochastic matrix: A Riemannian optimization approach
Fabio Durastante, Beatrice Meini
We propose two approaches, based on Riemannian optimization, for computing a stochastic approximation of the th root of a stochastic matrix . In the first approach, the appro…