Data-driven Control of Hypergraphs: Leveraging THIS to Damp Noise in Diffusive Hypergraphs

TL;DR

This paper introduces a data-driven framework that infers hypergraph structures from partial observations and designs controllers to steer complex systems with higher-order interactions, validated on Kuramoto oscillator networks.

Contribution

It combines hypergraph inference with control design, enabling control of systems with unknown multibody interactions using minimal controllable nodes.

Findings

Successfully infers hypergraph structures from partial data.

Designs controllers to steer systems toward desired states.

Validated on Kuramoto oscillator hypergraph network.

Abstract

Controllability determines whether a system's state can be guided toward any desired configuration, making it a fundamental prerequisite for designing effective control strategies. In the context of networked systems, controllability is a well-established concept. However, many real-world systems, from biological collectives to engineered infrastructures, exhibit higher-order interactions that cannot be captured by simple graphs. Moreover, the way in which agents interact and influence one another is often unknown and must be inferred from partial observations of the system. Here, we close the loop between a hypergraph representation and our recently developed hypergraph inference algorithm, THIS, to infer the underlying multibody couplings. Building on the inferred structure, we design a parsimonious controller that, given a minimal set of controllable nodes, steers the system toward a desired configuration. We validate the proposed system identification and control framework on a network of Kuramoto oscillators evolving over a hypergraph.