Adaptive control of dynamic networks

TL;DR

This paper introduces an adaptive control algorithm for dynamic networks that minimizes control cost and reconfiguration frequency without prior knowledge of network evolution, demonstrated through experiments on synthetic and real-world data.

Contribution

It proposes a novel adaptive control method with a node-level metric and partial matching strategy to efficiently manage control in evolving networks.

Findings

Reduces switching cost by 22% in synthetic networks

Reduces switching cost by 19% in real-world networks

Does not require future network evolution knowledge

Abstract

Real-world network systems are inherently dynamic, with network topologies undergoing continuous changes over time. Previous works often focus on static networks or rely on complete prior knowledge of evolving topologies, whereas real-world networks typically undergo stochastic structural changes that are difficult to predict in advance. To address this challenge, we define the adaptive control problem and propose an adaptive control algorithm to reduce the extra control cost caused by driver node switching. We introduce a node-level adaptive control metric to capture both the stability and consistency of each node across historical topologies. By integrating this metric with a partial matching repair strategy, our algorithm adjusts the minimum driver node set in real time at each snapshot, while minimizing unnecessary reconfigurations between consecutive time steps. Extensive experiments on synthetic and real-world dynamic networks demonstrate that the proposed adaptive control algorithm significantly outperforms the existing algorithm, reducing the switching cost by an average of 22% in synthetic networks and 19\% in real-world networks, without requiring foreknowledge of the future evolution of the network. These findings extend the theoretical scope of dynamic network controllability and open new avenues for practical applications in transportation, social, and molecular regulatory systems.