  Social network simulation plays a crucial role in addressing various
challenges within social science. It offers extensive applications such as
state prediction, phenomena explanation, and policy-making support, among
others. In this work, we harness the formidable human-like capabilities
exhibited 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 to
the widely employed agent-based simulation paradigm, we employ prompt
engineering and prompt tuning techniques to ensure that the agent's behavior
closely emulates that of a genuine human within the social network.
Specifically, we simulate three pivotal aspects: emotion, attitude, and
interaction behaviors. By endowing the agent in the system with the ability to
perceive the informational environment and emulate human actions, we observe
the emergence of population-level phenomena, including the propagation of
information, attitudes, and emotions. We conduct an evaluation encompassing two
levels of simulation, employing real-world social network data. Encouragingly,
the results demonstrate promising accuracy. This work represents an initial
step 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 the
development of simulation systems within, but not limited to, social science.
