# Parallel Machine Learning for Forecasting the Dynamics of Complex Networks

arXiv: https://arxiv.org/abs/2108.12129 · HTML: https://mlsanity.com/paper/2108.12129/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 24 references that are in the map.
- First posted on arXiv 27 Aug 2021.

## Builds on (most cited first, 3 of 3)

- [Rapid Time Series Prediction with a Hardware-Based Reservoir Computer](https://mlsanity.com/paper/1807.07627/) (2018)
- [Using Machine Learning to Assess Short Term Causal Dependence and Infer Network Links](https://mlsanity.com/paper/1912.02721/) (2019)
- [Link Prediction in Complex Networks: A Mutual Information Perspective](https://mlsanity.com/paper/1405.4341/) (2014)

## Built on by (most cited first, 17 of 17)

- [Catch-22s of reservoir computing](https://mlsanity.com/paper/2210.10211/) (2022)
- [Inferring Attracting Basins of Power System with Machine Learning](https://mlsanity.com/paper/2305.14374/) (2023)
- [Learn one size to infer all: Exploiting translational symmetries in delay-dynamical and spatio-temporal systems using scalable neural networks](https://mlsanity.com/paper/2111.03706/) (2021)
- [On instabilities in neural network-based physics simulators](https://mlsanity.com/paper/2406.13101/) (2024)
- [Learning to learn ecosystems from limited data -- a meta-learning approach](https://mlsanity.com/paper/2410.07368/) (2024)
- [Tailored Forecasting from Short Time Series via Meta-learning](https://mlsanity.com/paper/2501.16325/) (2025)
- [Learning Beyond Experience: Generalizing to Unseen State Space with Reservoir Computing](https://mlsanity.com/paper/2506.05292/) (2025)
- [Stretched and measured neural predictions of complex network dynamics](https://mlsanity.com/paper/2301.04900/) (2023)
- [Suppressing unknown disturbances to dynamical systems using machine learning](https://mlsanity.com/paper/2307.03690/) (2023)
- [Modeling Nonlinear Oscillator Networks Using Physics-Informed Hybrid Reservoir Computing](https://mlsanity.com/paper/2411.05867/) (2024)
- [Versatile Reservoir Computing for Heterogeneous Complex Networks](https://mlsanity.com/paper/2505.15219/) (2025)
- [ChaosNexus: A Foundation Model for ODE-based Chaotic System Forecasting with Hierarchical Multi-scale Awareness](https://mlsanity.com/paper/2509.21802/) (2025)
- [Detecting disturbances in network-coupled dynamical systems with machine learning](https://mlsanity.com/paper/2307.12771/) (2023)
- [Boosting Reservoir Computing with Brain-inspired Adaptive Dynamics](https://mlsanity.com/paper/2504.12480/) (2025)
- [PhyxMamba: Chaotic System Reconstruction from Short Context Observations with Generative State-Space Models](https://mlsanity.com/paper/2505.23863/) (2025)
- [Inferring bifurcation diagrams of two distinct chaotic systems by a single machine](https://mlsanity.com/paper/2604.26632/) (2026)
- [Causal Local States: Scalable Simultaneous Causal Network Inference and Forecasting for Dynamical Systems](https://mlsanity.com/paper/2608.17452/) (2026)

## 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.