发表机构
University of Victoria; Leiden University（维多利亚大学; 莱顿大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本综述首次系统梳理大语言模型在社交网络建模中的应用，将其分为网络生成与动态过程两类，并探讨了偏见与提示敏感性等挑战及未来方向。
AI 中文摘要
大语言模型（LLMs）正迅速崛起为一种通过自然语言表示用户及其关系和交互来建模社交网络的新范式。与经典网络模型或深度学习方法不同，LLMs能够模拟情境感知的社会行为和语言驱动的交互，从而实现对网络形成和动态社会过程更为逼真的建模。然而，现有研究分散于不同的研究社区，缺乏统一的视角。本综述首次对用于社交网络建模的LLMs进行了全面回顾，将文献组织为两大类：网络生成模型和动态过程模型。网络生成模型进一步分为基于选择的与基于交互的方法，而动态过程模型则归类为观点动力学、信息扩散和谣言传播，并分别阐述其底层建模机制。LLMs实现了丰富的文本社交交互和决策，但也表现出诸多局限性，包括固有的社会偏见和提示敏感性。我们概述了这些开放的研究挑战，并讨论了基于LLM的社交网络建模的未来方向。
英文摘要
Large language models (LLMs) are rapidly emerging as a new paradigm for modeling social networks by representing users and their relationships and interactions through natural language. Unlike classical network models or deep learning approaches, LLMs can simulate context-aware social behavior and language-driven interactions, enabling more realistic modeling of network formation and dynamic social processes. However, existing studies are scattered across different research communities and lack a unified perspective. This survey presents the first comprehensive review of LLMs for social network modeling by organizing the literature into two broad categories: network generative models and dynamic process models. Network generative models are further classified into selection-based and interaction-based approaches, while dynamic process models are categorized into opinion dynamics, information diffusion, and rumor propagation, each with their underlying modeling mechanisms. LLMs enable rich textual social interactions and decision-making, but they also exhibit many limitations, including inherent social biases and prompt sensitivity. We outline these open research challenges and discuss future directions in LLM-based social network modeling.
