  Recent advances in generative artificial intelligence (AI), and particularly
the integration of large language models (LLMs), have had considerable impact
on multiple domains. Meanwhile, enhancing dynamic network performance is a
crucial element in promoting technological advancement and meeting the growing
demands of users in many applications areas involving networks. In this
article, we explore an integration of LLMs and graphs in dynamic networks,
focusing on potential applications and a practical study. Specifically, we
first review essential technologies and applications of LLM-enabled graphs,
followed by an exploration of their advantages in dynamic networking.
Subsequently, we introduce and analyze LLM-enabled graphs and their
applications in dynamic networks from the perspective of LLMs as different
roles. On this basis, we propose a novel framework of LLM-enabled graphs for
networking optimization, and then present a case study on UAV networking,
concentrating on optimizing UAV trajectory and communication resource
allocation to validate the effectiveness of the proposed framework. Finally, we
outline several potential future extensions.
