Parallel Machine Learning for Forecasting the Dynamics of Complex Networks

• URL: http://arxiv.org/abs/2108.12129v1
• Date: Fri, 27 Aug 2021 06:06:41 GMT
• Title: Parallel Machine Learning for Forecasting the Dynamics of Complex Networks
• Authors: Keshav Srinivasan, Nolan Coble, Joy Hamlin, Thomas Antonsen, Edward Ott and Michelle Girvan
• Abstract summary: We present a machine learning scheme for forecasting the dynamics of large complex networks. We use a parallel architecture that mimics the topology of the network of interest.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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