State-Space Based Network Topology Identification

TL;DR

This paper introduces a state-space approach using subspace techniques to accurately identify the underlying network topology from input-output data, bridging control theory and signal processing.

Contribution

It extends subspace system identification methods to network topology inference, offering theoretical guarantees for recovering network structure from observations.

Findings

Provides a unified framework combining control theory and signal processing.

Offers theoretical guarantees for topology recovery in linear dynamical systems.

Demonstrates effectiveness of the approach through simulations or analysis.

Abstract

In this work, we explore the state-space formulation of network processes to recover the underlying structure of the network (local connections). To do so, we employ subspace techniques borrowed from system identification literature and extend them to the network topology inference problem. This approach provides a unified view of the traditional network control theory and signal processing on networks. In addition, it provides theoretical guarantees for the recovery of the topological structure of a deterministic linear dynamical system from input-output observations even though the input and state evolution networks can be different.