Social network simulation is developed to provide a comprehensive understanding of social networks in the real world, which can be leveraged for a wide range of applications such as group behavior emergence, policy optimization, and business strategy development. However, billions of individuals and their evolving interactions involved in social networks pose challenges in accurately reflecting real-world complexities. In this study, we propose a comprehensive Social Network Simulation System (GA-S3) that leverages newly designed Group Agents to make intelligent decisions regarding various online events. Unlike other intelligent agents that represent an individual entity, our group agents model a collection of individuals exhibiting similar behaviors, facilitating the simulation of large-scale network phenomena with complex interactions at a manageable computational cost. Additionally, we have constructed a social network benchmark from 2024 popular online events that contains fine-grained information on Internet traffic variations. The experiment demonstrates that our approach is capable of achieving accurate and highly realistic prediction results. Code is open at https://github.com/AI4SS/GAS-3.

翻译：社交网络仿真的发展旨在全面理解现实世界中的社交网络，其可广泛应用于群体行为涌现、政策优化及商业策略制定等领域。然而，社交网络中涉及的数十亿个体及其持续演化的交互关系，对精确反映现实世界的复杂性提出了挑战。本研究提出了一种综合性社交网络仿真系统（GA-S3），该系统利用新设计的群体智能体对各种在线事件做出智能决策。与代表单个实体的其他智能体不同，我们的群体智能体对表现出相似行为的个体集合进行建模，从而在可管理的计算成本下，促进了具有复杂交互的大规模网络现象的仿真。此外，我们基于2024年的热门在线事件构建了一个社交网络基准数据集，该数据集包含了网络流量变化的细粒度信息。实验表明，我们的方法能够实现准确且高度逼真的预测结果。代码已在 https://github.com/AI4SS/GAS-3 开源。