LLMs for Social Network Modeling: From Network Generation to Dynamic Processes
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 ...
| Authors | Shikha Mallick, Alex Thomo, Akrati Saxena |
|---|---|
| Primary category | cs.SI |
| Published | 7 September 2026 |
| Last updated at source | 7 September 2026 |
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