Network control by a constrained external agent as a continuous optimization problem

TL;DR

This paper introduces a deep-learning based optimization framework for controlling socioeconomic networks, specifically corporate control, under real-world constraints, enabling more effective interventions and policy insights.

Contribution

It combines deep-learning optimization with network science to improve control strategies in real-world socioeconomic networks, addressing a key gap in existing methods.

Findings

Framework effectively identifies vulnerabilities in corporate networks.

Enables optimization of interventions under real-world constraints.

Provides policy-relevant insights into network control and vulnerability.

Abstract

Social science studies dealing with control in networks typically resort to heuristics or describing the static control distribution. Optimal policies, however, require interventions that optimize control over a socioeconomic network subject to real-world constraints. We integrate optimisation tools from deep-learning with network science into a framework that is able to optimize such interventions in real-world networks. We demonstrate the framework in the context of corporate control, where it allows to characterize the vulnerability of strategically important corporate networks to sensitive takeovers, an important contemporaneous policy challenge. The framework produces insights that are relevant for governing real-world socioeconomic networks, and opens up new research avenues for improving our understanding and control of such complex systems.