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Social network simulation plays a crucial role in addressing variouschallenges within social science. It offers extensive applications such asstate prediction, phenomena explanation, and policy-making support, amongothers. In this work, we harness the formidable human-like capabilitiesexhibited by large language models (LLMs) in sensing, reasoning, and behaving,and utilize these qualities to construct the S$^3$ system (short for$\textbf{S}$ocial network $\textbf{S}$imulation $\textbf{S}$ystem). Adhering tothe widely employed agent-based simulation paradigm, we employ promptengineering and prompt tuning techniques to ensure that the agent's behaviorclosely emulates that of a genuine human within the social network.Specifically, we simulate three pivotal aspects: emotion, attitude, andinteraction behaviors. By endowing the agent in the system with the ability toperceive the informational environment and emulate human actions, we observethe emergence of population-level phenomena, including the propagation ofinformation, attitudes, and emotions. We conduct an evaluation encompassing twolevels of simulation, employing real-world social network data. Encouragingly,the results demonstrate promising accuracy. This work represents an initialstep in the realm of social network simulation empowered by LLM-based agents.We anticipate that our endeavors will serve as a source of inspiration for thedevelopment of simulation systems within, but not limited to, social science.

S3: Social-network Simulation System with Large Language Model-Empowered Agents