Control Distance and Energy Scaling of Complex Networks

TL;DR

This paper explains the large variance in control energy in complex networks by analyzing path length and redundancy, providing bounds and foundations for improved energy estimation.

Contribution

It introduces exact and refined upper bounds for control energy based on path length and redundancy, advancing understanding of control in complex networks.

Findings

Control energy scales exponentially with subset size.

Path length and redundancy significantly influence energy variance.

Refined bounds improve estimation accuracy by an order of magnitude.

Abstract

It has recently been shown that the average energy required to control a subset of nodes in a complex network scales exponentially with the cardinality of the subset. While the mean scales exponentially, the variance of the control energy over different subsets of nodes is large and has as of yet not been explained. Here, we provide an explanation of the large variance as a result of both the length of the path that connects control inputs to the target nodes and the redundancy of paths of shortest length. Our first result provides an exact upper bound of the control energy as a function of path length between driver node and target node along an infinite path graph. We also show that the energy estimation is still very accurate even when finite size effects are taken into account. Our second result refines the upper bound that takes into account not only the length of the path, but also the redundancy of paths. We show that it improves the upper bound approximation by an order of magnitude or more. Finally, we lay out the foundations for a more accurate estimation of the control energy for the multi-target and multi-driver problem.