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arXiv · 2108.12129

Parallel Machine Learning for Forecasting the Dynamics of Complex Networks

Keshav Srinivasan·Nolan Coble·Joy Hamlin·Thomas Antonsen·Edward Ott·Michelle Girvan
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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.

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Keshav Srinivasan, Nolan Coble, Joy Hamlin, Thomas Antonsen, Edward Ott, Michelle Girvan. 2021-08-27. Parallel Machine Learning for Forecasting the Dynamics of Complex Networks. https://doi.org/10.1103/physrevlett.128.164101

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