Recovering sparse networks: Basis adaptation and stability under extensions
Dynamical Systems
2021-06-02 v1 Adaptation and Self-Organizing Systems
Abstract
We consider the problem of recovering equations of motion from multivariate time series of oscillators interacting on sparse networks. We reconstruct the network from an initial guess which can include expert knowledge about the system such as main motifs and hubs. When sparsity is taken into account the number of data points needed is drastically reduced when compared to the least-squares recovery. We show that the sparse solution is stable under basis extensions, that is, once the correct network topology is obtained, the result does not change if further motifs are considered.
Cite
@article{arxiv.2104.00796,
title = {Recovering sparse networks: Basis adaptation and stability under extensions},
author = {Marcel Novaes and Edmilson Roque dos Santos and Tiago Pereira},
journal= {arXiv preprint arXiv:2104.00796},
year = {2021}
}
Comments
22 pages, 5 figure