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交互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。大语言模型的最新进展使得多智能体框架具备类人推理与通信能力。然而，现有基于大语言模型的模拟将社会网络视为固定的通信支架，未能利用塑造真实系统中行为趋同与异质性影响力的结构信号，这常导致模拟低效且动力学不真实。为应对这一挑战，我们提出TopoSim——一个统一的拓扑感知社会模拟框架，该框架在两个互补维度上显式地将结构推理融入智能体交互。首先，TopoSim将结构角色与交互上下文相似的智能体对齐至共享主干单元，实现协同更新，在保持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，引入基于网络拓扑而非均匀影响力假设的异质性交互模式。在三个社会模拟框架及多种数据集上的广泛实验表明，TopoSim在实现相当或更高模拟保真度的同时，将令牌消耗降低50-90%。此外，我们的方法更准确地复现了真实社会系统中观测到的关键结构现象，并展现出强泛化性与可扩展性。