Reconstructing dynamical networks via feature ranking
This addresses the need for network reconstruction methods in complex systems with minimal assumptions, though it appears incremental as it applies existing ML techniques to this domain.
The authors tackled the problem of reconstructing network topology from time-resolved data without strong assumptions, using machine learning feature ranking methods like Random forest and RReliefF, and found the method robust to factors like coupling strength and noise but dependent on dynamical regime.
Empirical data on real complex systems are becoming increasingly available. Parallel to this is the need for new methods of reconstructing (inferring) the topology of networks from time-resolved observations of their node-dynamics. The methods based on physical insights often rely on strong assumptions about the properties and dynamics of the scrutinized network. Here, we use the insights from machine learning to design a new method of network reconstruction that essentially makes no such assumptions. Specifically, we interpret the available trajectories (data) as features, and use two independent feature ranking approaches -- Random forest and RReliefF -- to rank the importance of each node for predicting the value of each other node, which yields the reconstructed adjacency matrix. We show that our method is fairly robust to coupling strength, system size, trajectory length and noise. We also find that the reconstruction quality strongly depends on the dynamical regime.