# Parallel Machine Learning for Forecasting the Dynamics of Complex Networks

@article{Srinivasan2022ParallelML, title={Parallel Machine Learning for Forecasting the Dynamics of Complex Networks}, author={Keshav Srinivasan and Nolan J. Coble and Joyce L. Hamlin and Thomas M. Antonsen and Edward Ott and Michelle Girvan}, journal={Physical review letters}, year={2022}, volume={128 16}, pages={ 164101 } }

Forecasting the dynamics of large, complex, sparse 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…

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