Graph-Based Alternatives to LLMs for Human Simulation

Joseph Suh; Suhong Moon; Serina Chang

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arXiv · 2511.02135

Graph-Based Alternatives to LLMs for Human Simulation

Joseph Suh·Suhong Moon·Serina Chang

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Abstract

Large language models (LLMs) have become a popular approach for simulating human behaviors, yet it remains unclear if LLMs are necessary for all simulation tasks. We study a broad family of close-ended simulation tasks, with applications from survey prediction to test-taking, and show that a graph neural network can match or surpass strong LLM-based methods. We introduce Graph-basEd Models for Human Simulation (GEMS) which formulates close-ended simulation as link prediction on a heterogeneous graph of individuals and choices. Across three datasets and three evaluation settings, GEMS matches or outperforms the strongest LLM-based methods while using three orders of magnitude fewer parameters. These results suggest that graph-based modeling can complement LLMs as an efficient and transparent approach to simulating human behaviors. Code is available at https://github.com/schang-lab/gems.

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Joseph Suh, Suhong Moon, Serina Chang. 2025-11-03. Graph-Based Alternatives to LLMs for Human Simulation. https://arxiv.org/abs/2511.02135

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