Network Growth with Feedback

TL;DR

This paper introduces a dynamic network growth model using feedback mechanisms to adapt parameters based on the current network state, resulting in larger and more efficient networks.

Contribution

It presents a novel framework for network growth that incorporates feedback to dynamically adjust parameters, extending beyond fixed-parameter models.

Findings

Feedback enables larger, more efficient networks.

Linear resource scaling causes a transition to a condensed state.

Sublinear scaling delays network condensation.

Abstract

Existing models of network growth typically have one or two parameters or strategies which are fixed for all times. We introduce a general framework where feedback on the current state of a network is used to dynamically alter the values of such parameters. A specific model is analyzed where limited resources are shared amongst arriving nodes, all vying to connect close to the root. We show that tunable feedback leads to growth of larger, more efficient networks. Exact results show that linear scaling of resources with system size yields crossover to a trivial condensed state, which can be considerably delayed with sublinear scaling.