Social simulation is essential for understanding collective human behavior by modeling how individual interactions give rise to large-scale social dynamics. Recent advances in large language models (LLMs) have enabled multi-agent frameworks with human-like reasoning and communication capabilities. However, existing LLM-based simulations treat social networks as fixed communication scaffolds, failing to leverage the structural signals that shape behavioral convergence and heterogeneous influence in real-world systems, which often leads to inefficient and unrealistic dynamics. To address this challenge, we propose TopoSim, a unified topology-aware social simulation framework that explicitly integrates structural reasoning into agent interactions along two complementary dimensions. First, TopoSim aligns agents with similar structural roles and interaction contexts into shared backbone units, enabling coordinated updates that reduce redundant computation while preserving emergent social dynamics. Second, TopoSim models social influence as a structure-induced signal, introducing heterogeneous interaction patterns grounded in network topology rather than uniform influence assumptions. Extensive experiments across three social simulation frameworks and diverse datasets demonstrate that TopoSim achieves comparable or improved simulation fidelity while reducing token consumption by 50 - 90%. Moreover, our approach more accurately reproduces key structural phenomena observed in real-world social systems and exhibits strong generalization and scalability.

翻译：社会模拟通过建模个体互动如何引发大规模社会动态，对于理解集体人类行为至关重要。大语言模型（LLM）的最新进展使多智能体框架具备类人推理与通信能力。然而，现有基于LLM的模拟将社会网络视为固定通信支架，未能利用塑造真实系统行为趋同与异质性影响力的结构信号，常导致低效且不真实的动力学。为解决这一挑战，我们提出TopoSim——一个统一的拓扑感知社会模拟框架，沿两个互补维度将结构推理显式整合至智能体交互中。首先，TopoSim将具有相似结构角色与交互上下文的智能体对齐至共享骨干单元，实现协调更新，在保留涌现社会动力学的同时减少冗余计算。其次，TopoSim将社会影响力建模为结构诱导信号，引入基于网络拓扑的异质性交互模式，而非均匀影响力假设。跨三个社会模拟框架及多样化数据集的广泛实验表明，TopoSim在实现相当或更优模拟保真度的同时，将令牌消耗降低50-90%。此外，我们的方法更精确地再现了真实社会系统中观察到的关键结构现象，并展现出强大的泛化性与可扩展性。