Parallel Machine Learning for Forecasting the Dynamics of Complex Networks
This addresses forecasting challenges in complex networks, but it is incremental as it builds on reservoir computing with a parallel architecture.
The authors tackled forecasting dynamics in large complex networks by developing a parallel machine learning scheme that mimics network topology, demonstrating its utility and scalability on a chaotic oscillator network with both known and inferred links.
Forecasting the dynamics of large complex networks from previous time-series data is important in a wide range of contexts. Here we present a machine learning scheme for this task using a parallel architecture that mimics the topology of the network of interest. We demonstrate the utility and scalability of our method implemented using reservoir computing on a chaotic network of oscillators. Two levels of prior knowledge are considered: (i) the network links are known; and (ii) the network links are unknown and inferred via a data-driven approach to approximately optimize prediction.