# Optimization in Gradient Networks

## TL;DR

This paper introduces a Monte Carlo optimization method to reduce congestion in gradient networks with scale-free and Erdős-Rényi structures, improving their efficiency for transport and dynamical processes.

## Contribution

It presents a novel optimization approach that alters the structure of gradient networks to minimize jamming, a significant advancement over previous studies on random gradient networks.

## Key findings

- Structural correlations reduce network congestion.
- Optimization alters degree distribution and structural properties.
- Results improve understanding of transport in complex networks.

## Abstract

Gradient networks can be used to model the dominant structure of complex networks. Previous works have focused on random gradient networks. Here we study gradient networks that minimize jamming on substrate networks with scale-free and Erd\H{o}s-R\'enyi structure. We introduce structural correlations and strongly reduce congestion occurring on the network by using a Monte Carlo optimization scheme. This optimization alters the degree distribution and other structural properties of the resulting gradient networks. These results are expected to be relevant for transport and other dynamical processes in real network systems.