Task-Dependent Weighted Average Energy Controllability Score for Network Intervention

TL;DR

This paper introduces a task-dependent controllability score for network interventions that incorporates transition priorities, providing a more tailored and interpretable measure for identifying key nodes in large-scale systems.

Contribution

It proposes the weighted average energy controllability score (W-AECS), extending AECS with transition-dependent weights, supported by control-theoretic interpretation and demonstrated on brain network data.

Findings

W-AECS admits a control-theoretic interpretation via expected minimum-energy steering.

The proposed score is strictly convex and has a unique solution.

Application to brain networks shows transition-dependent weighting alters scoring patterns.

Abstract

Controllability scores provide principled information on where intervention should be applied in large-scale network systems when explicit control design is difficult. Two representative controllability scores are the volumetric controllability score (VCS) and the average energy controllability score (AECS). While both are important, the standard AECS treats all state-transition directions uniformly. In this paper, we propose the weighted average energy controllability score (W-AECS), a task-dependent extension of AECS that incorporates a prescribed transition of interest through a weighting matrix. We show that the proposed formulation admits a control-theoretic interpretation via expected minimum-energy steering, and establish strict convexity and generic uniqueness. These results support the interpretation of W-AECS as a well-defined node-wise task-dependent intervention score. We also illustrate the proposed method on a structural brain-network dataset, where transition-dependent weighting reshapes the scoring pattern, yielding a VCS-like preference among the highest-ranked regions while preserving an overall structure distinct from both standard AECS and VCS.