Successful strategies for competing networks

TL;DR

This paper derives spectral-based rules to understand and improve competitive interactions in various networked systems, revealing how internal and external network structures influence outcomes and offering strategies for better competition.

Contribution

It introduces a spectral approach to analyze competitive dynamics in networks, providing new insights into how network structure affects competition outcomes and strategies for improvement.

Findings

Network structure influences competition outcomes significantly.

Spectral properties can predict the winner and time to victory.

Strategies can be devised to optimize competitive success.

Abstract

Competitive interactions represent one of the driving forces behind evolution and natural selection in biological and sociological systems. For example, animals in an ecosystem may vie for food or mates; in a market economy, firms may compete over the same group of customers; sensory stimuli may compete for limited neural resources in order to enter the focus of attention. Here, we derive rules based on the spectral properties of the network governing the competitive interactions between groups of agents organized in networks. In the scenario studied here the winner of the competition, and the time needed to prevail, essentially depend on the way a given network connects to its competitors and on its internal structure. Our results allow assessing the extent to which real networks optimize the outcome of their interaction, but also provide strategies through which competing networks can improve on their situation. The proposed approach is applicable to a wide range of systems that can be modeled as networks.