# Realistic Control of Network Dynamics

arXiv: https://arxiv.org/abs/1307.0015 · HTML: https://mlsanity.com/paper/1307.0015/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2013.

## Built on by (most cited first, 5 of 5)

- [Implicit energy regularization of neural ordinary-differential-equation control](https://mlsanity.com/paper/2103.06525/) (2021)
- [Energy-Based Dynamical Models for Neurocomputation, Learning, and Optimization](https://mlsanity.com/paper/2604.05042/) (2026)
- [Emergence Transformer: Dynamical Temporal Attention Matters](https://mlsanity.com/paper/2604.19816/) (2026)
- [Cell reprogramming design by transfer learning of functional transcriptional networks](https://mlsanity.com/paper/2403.04837/) (2024)
- [A PBN-RL-XAI Framework for Discovering a "Hit-and-Run" Therapeutic Strategy in Melanoma](https://mlsanity.com/paper/2507.10136/) (2025)

## Abstract (arXiv)

The control of complex networks is of paramount importance in areas as diverse as ecosystem management, emergency response, and cell reprogramming. A fundamental property of networks is that perturbations to one node can affect other nodes, potentially causing the entire system to change behavior or fail. Here, we show that it is possible to exploit the same principle to control network behavior. Our approach accounts for the nonlinear dynamics inherent to real systems, and allows bringing the system to a desired target state even when this state is not directly accessible due to constraints that limit the allowed interventions. Applications show that this framework permits reprogramming a network to a desired task as well as rescuing networks from the brink of failure---which we illustrate through the mitigation of cascading failures in a power-grid network and the identification of potential drug targets in a signaling network of human cancer.