Fiedler-Based Characterization and Identification of Leaders in Semi-Autonomous Networks

Abstract: This paper addresses the problem of identifying leader nodes in semi-autonomous consensus networks from observed agent dynamics. Using the grounded Laplacian formulation, we derive spectral conditions that ensure the components of the Fiedler vector associated with leader and follower nodes are distinct. Building on the foundation, we emply the notion of relative tempo from prio works as an observable quantity that relates agents' steady-state velocities to the Fiedler vector. This relationship enables the development of a data-driven algorithm that reconstructs the Fiedler vector - and consequently identifies the leader set - using only steady-state velocity measurements, without requiring knowledge of the network topology. The proposed approach is validated through nuerical examples, demonstrating how spectral properties and relative tempo measurements can be combined to reveal hidden leadership structures in consensus networks.