LLMs for Social Network Analysis: Mapping Relationships from Unstructured Survey Response ^†

This paper explores the emerging potential of large language models (LLMs) and generative AI for social network analysis (SNA) based on open-ended survey data as a source. We introduce a novel methodology, Survey-to-Multilayer Network (SURVEY2MLN), which systematically transforms qualitative survey responses into structured multilayer social networks. The proposed approach integrates prompt engineering with LLM-based text interpretation to extract entities and infer relationships, formalizing them as distinct network layers representing research similarity, communication, and organizational affiliation. The SURVEY2MLN methodology is defined through six phases, including data preprocessing, prompt-based extraction, network construction, integration, analysis, and validation. We demonstrate its application through a real-world case study within an academic department, where prompt engineering was used to extract and model relational data from narrative responses. The resulting multilayer network reveals both explicit and latent social structures that are not accessible through conventional survey techniques. Our results show that LLMs can serve as effective tools for deriving sociograms from free-form text and highlight the potential of AI-driven methods to advance SNA into new, text-rich domains of inquiry.