Switching Network System Identification via Convex Optimizations

TL;DR

This paper presents a convex optimization approach for identifying switched network systems, accurately recovering node dynamics and graph topologies from data without prior mode labels.

Contribution

It extends convex identification methods to structured network systems, enabling simultaneous recovery of dynamics and switching topologies from sampled data.

Findings

Accurately recovers node dynamics and network topologies

Works without prior knowledge of mode labels

Demonstrated on diffusively coupled oscillators

Abstract

This paper introduces a convex optimization framework for identifying switched network systems, in which both the node dynamics and the underlying graph topology switch between a finite number of configurations. Building on our recent convex identification method for general switching systems, we extend the formulation to structured network systems where each mode corresponds to a distinct adjacency matrix. We show that both the continuous node dynamics and binary network topologies can be identified from sampled state-velocity data by solving a sequence of convex programs. The proposed framework provides a unified and scalable way to recover piecewise network structures from data without a prior knowledge of mode labels at each state. Numerical results on diffusively coupled oscillators demonstrate accurate recovery of both mode dynamics and switching graphs.