Unexpected advantages of exploitation for target searches in complex networks

TL;DR

This paper reveals that exploitation, revisiting previous experiences, can enhance or hinder search efficiency depending on network structure, with significant implications for understanding search behaviors in complex systems.

Contribution

It introduces a non-Markovian random walk model to analyze exploitation effects and identifies network structures where exploitation improves search performance.

Findings

Exploitation improves search in lollipop-like networks.

Exploitation hinders search in clique-like networks.

Exploitation reduces exploration time in real-world networks.

Abstract

Exploitation universally emerges in various decision-making contexts, e.g., animals foraging, web surfing, the evolution of scientists' research topics, and our daily lives. Despite its ubiquity, exploitation, which refers to the behavior of revisiting previous experiences, has often been considered to delay the search process of finding a target. In this paper, we investigate how exploitation affects search performance by applying a non-Markovian random walk model, where a walker randomly revisits a previously visited node using long-term memory. We analytically study two broad forms of network structures, namely (i) clique-like networks and (ii) lollipop-like networks, and find that exploitation can significantly improve search performance in lollipop-like networks whereas it hinders target search in clique-like networks. Moreover, we numerically verify that exploitation can reduce the time needed to fully explore the underlying networks by using $550$ diverse real-world networks. Based on the analytic result, we define the lollipop-likeness of a network and observe a positive relationship between the advantage of exploitation and lollipop-likeness.