mlsanityData to 7 Oct 2026

Parallel Machine Learning for Forecasting the Dynamics of Complex Networks

arXiv 2108.12129 ↗First posted 27 Aug 2021cs.LG nlin.CDcited by 17

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Abstract (arXiv)

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.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 3 of its 24 references that are in the map. First posted on arXiv 27 Aug 2021.

Builds on

3 of 24 references.

References · 3

  1. 2018Rapid Time Series Prediction with a Hardware-Based Reservoir Computercited by 14
  2. 2019Using Machine Learning to Assess Short Term Causal Dependence and Infer Network Linkscited by 10
  3. 2014Link Prediction in Complex Networks: A Mutual Information Perspectivecited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 1; 2023: 4; 2024: 3; 2025: 6; 2026: 2.

17 papers.

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  1. 2022Catch-22s of reservoir computingcited by 11
  2. 2023Inferring Attracting Basins of Power System with Machine Learningcited by 4
  3. 2021Learn one size to infer all: Exploiting translational symmetries in delay-dynamical and spatio-temporal systems using scalable neural networkscited by 3
  4. 2024On instabilities in neural network-based physics simulatorscited by 2
  5. 2024Learning to learn ecosystems from limited data -- a meta-learning approachcited by 2
  6. 2025Tailored Forecasting from Short Time Series via Meta-learningcited by 2
  7. 2025Learning Beyond Experience: Generalizing to Unseen State Space with Reservoir Computingcited by 2
  8. 2023Stretched and measured neural predictions of complex network dynamicscited by 1
  9. 2023Suppressing unknown disturbances to dynamical systems using machine learningcited by 1
  10. 2024Modeling Nonlinear Oscillator Networks Using Physics-Informed Hybrid Reservoir Computingcited by 1
  11. 2025Versatile Reservoir Computing for Heterogeneous Complex Networkscited by 1
  12. 2025ChaosNexus: A Foundation Model for ODE-based Chaotic System Forecasting with Hierarchical Multi-scale Awarenesscited by 1
5 more
  1. 2023Detecting disturbances in network-coupled dynamical systems with machine learningcited by 0
  2. 2025Boosting Reservoir Computing with Brain-inspired Adaptive Dynamicscited by 0
  3. 2025PhyxMamba: Chaotic System Reconstruction from Short Context Observations with Generative State-Space Modelscited by 0
  4. 2026Inferring bifurcation diagrams of two distinct chaotic systems by a single machinecited by 0
  5. 2026Causal Local States: Scalable Simultaneous Causal Network Inference and Forecasting for Dynamical Systemscited by 0