  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.
