LLMs for Social Network Modeling: From Network Generation to Dynamic Processes

2026-09-07

AI Summary

本文综述了大型语言模型在社交网络建模中的应用，通过自然语言处理方法模拟用户关系和互动，分为网络生成模型和动态过程模型两类，并讨论了存在的局限性和未来研究方向。

Abstract

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.

Problem

• Large language models
• social network modeling
• network generative models
• dynamic process models
• social biases

Innovation

• large language models
• social network modeling
• context-aware social behavior
• network generative models
• dynamic process models