标题： 节点之间的LLMs：超越向量的社区发现 显示英文标题

标题： LLMs Between the Nodes: Community Discovery Beyond Vectors

摘要： 社区检测在社交网络图中起着至关重要的作用，有助于揭示群体动态、影响力路径和信息的传播。传统方法主要关注图的结构属性，但大型语言模型（LLMs）的最新进展为将语义和上下文信息整合到这一任务中开辟了新的途径。在本文中，我们详细研究了各种基于LLM的方法在识别社交图中的社区方面的表现。我们引入了一个称为CommLLM的两步框架，该框架利用GPT-4o模型以及基于提示的推理，将语言模型的输出与图结构融合。评估在六个真实世界的社交网络数据集上进行，使用归一化互信息（NMI）、调整兰德指数（ARI）、信息变化（VOI）和聚类纯度等关键指标来衡量性能。我们的发现表明，当由图感知策略引导时，LLMs可以成功应用于小型到中型图的社区检测任务。我们观察到，指令调优模型和精心设计的提示的结合显著提高了检测到的社区的准确性和一致性。这些见解不仅突显了LLMs在基于图的研究中的潜力，也强调了针对图数据特定结构定制模型交互的重要性。 显示英文摘要

摘要： Community detection in social network graphs plays a vital role in uncovering group dynamics, influence pathways, and the spread of information. Traditional methods focus primarily on graph structural properties, but recent advancements in Large Language Models (LLMs) open up new avenues for integrating semantic and contextual information into this task. In this paper, we present a detailed investigation into how various LLM-based approaches perform in identifying communities within social graphs. We introduce a two-step framework called CommLLM, which leverages the GPT-4o model along with prompt-based reasoning to fuse language model outputs with graph structure. Evaluations are conducted on six real-world social network datasets, measuring performance using key metrics such as Normalized Mutual Information (NMI), Adjusted Rand Index (ARI), Variation of Information (VOI), and cluster purity. Our findings reveal that LLMs, particularly when guided by graph-aware strategies, can be successfully applied to community detection tasks in small to medium-sized graphs. We observe that the integration of instruction-tuned models and carefully engineered prompts significantly improves the accuracy and coherence of detected communities. These insights not only highlight the potential of LLMs in graph-based research but also underscore the importance of tailoring model interactions to the specific structure of graph data.