# Networkcontrology

arXiv: https://arxiv.org/abs/1510.08320 · HTML: https://mlsanity.com/paper/1510.08320/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2015.

## Built on by (most cited first, 3 of 3)

- [A Learning Convolutional Neural Network Approach for Network Robustness Prediction](https://mlsanity.com/paper/2203.10552/) (2022)
- [A Graph Transformer-Driven Approach for Network Robustness Learning](https://mlsanity.com/paper/2306.06913/) (2023)
- [Temperature-driven inversion and nonlinear dynamics in ChatGPT-like AIs](https://mlsanity.com/paper/2608.00939/) (2026)

## Abstract (arXiv)

An increasing number of complex systems are now modeled as networks of coupled dynamical entities. Nonlinearity and high-dimensionality are hallmarks of the dynamics of such networks but have generally been regarded as obstacles to control. Here I discuss recent advances on mathematical and computational approaches to control high-dimensional nonlinear network dynamics under general constraints on the admissible interventions. I also discuss the potential of network control to address pressing scientific problems in various disciplines.