Identifying critical nodes in networks is a classical decision-making task,
and many methods struggle to strike a balance between adaptability and utility.
Therefore, we propose an approach that empowers Evolutionary Algorithm (EA)
with Large Language Models (LLMs), to generate a function called "score\_nodes"
which can further be used to identify crucial nodes based on their assigned
scores. Our model consists of three main components: Manual Initialization,
Population Management, and LLMs-based Evolution. It evolves from initial
populations with a set of designed node scoring functions created manually.
LLMs leverage their strong contextual understanding and rich programming skills
to perform crossover and mutation operations on the individuals, generating
excellent new functions. These functions are then categorized, ranked, and
eliminated to ensure the stable development of the populations while preserving
diversity. Extensive experiments demonstrate the excellent performance of our
method, showcasing its strong generalization ability compared to other
state-of-the-art algorithms. It can consistently and orderly generate diverse
and efficient node scoring functions. All source codes and models that can
reproduce all results in this work are publicly available at this link:
https://anonymous.4open.science/r/LLM4CN-6520

通过将进化算法（EA）与大型语言模型（LLMs）相结合，我们提出一种名为 “score_nodes” 函数的方法，用于根据它们的得分来识别关键节点。我们的模型通过手动初始化、种群管理和基于 LLMs 的演化三个主要组件来演化，展示了与其他现有算法相比的强的泛化能力，并且能持续有序地生成多样且高效的节点评分函数。