Parallel Machine Learning for Forecasting the Dynamics of Complex Networks

TL;DR

This paper introduces a parallel machine learning approach, based on reservoir computing, for forecasting the dynamics of large complex networks, demonstrating its effectiveness and scalability with chaotic oscillator networks.

Contribution

It presents a novel parallel architecture that mimics network topology for improved forecasting, applicable with known or inferred network links.

Findings

Effective forecasting of chaotic network dynamics demonstrated.

Scalable approach suitable for large networks.

Method works with both known and inferred network links.

Abstract

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.