Parallel Machine Learning for Forecasting the Dynamics of Complex Networks
Parallel Machine Learning for Forecasting the Dynamics of Complex Networks
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DOI：
10.1103/physrevlett.128.164101
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发表时间：
2021-08
影响因子：
8.6
通讯作者：
Keshav Srinivasan;Nolan J. Coble;J. Hamlin;T. Antonsen;E. Ott;M. Girvan
中科院分区：
文献类型：
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作者：
Keshav Srinivasan;Nolan J. Coble;J. Hamlin;T. Antonsen;E. Ott;M. Girvan
从之前的时间序列数据预测大型、复杂、稀疏网络的动态在很多情况下都很重要。在这里，我们提出了一种用于此任务的机器学习方案，使用模拟感兴趣网络拓扑的并行架构。我们展示了在混沌振荡器网络上使用储层计算实现的方法的实用性和可扩展性。考虑两个级别的先验知识：（i）网络链接已知，以及（ii）网络链接未知并通过数据驱动方法推断以近似优化预测。
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 network links are unknown and inferred via a data-driven approach to approximately optimize prediction.