Large-scale social simulators are essential for studying complex social patterns.Prior work explores hybrid methods to scale up simulations, combining large language models (LLM)-based agents with numerical agentbased models (ABM).However, this incurs high latency due to expensive memory retrieval and sequential ABM execution.To address this challenge, we propose GASim, a graph-accelerated hybrid multi-agent framework for large-scale social simulations.For core agents driven by LLM, GASim introduces Graph-Optimized Memory (GOM) to replace intensive LLM-based retrieval pipelines with lightweight propagation over a sparse memory graph.For the majority of ordinary agents, GASim employs Graph Message Passing (GMP), substituting sequential ABM execution with parallel updates by fine-grained feature aggregation and Graph Attention Network.We further introduce Entropy-Driven Grouping (EDG) that coordinates this hybrid partitioning, leveraging information entropy to dynamically identify emergent core agents situated in information-diverse neighborhoods.Extensive experiments show that GASim not only delivers a substantial 9.94× end-to-end speedup over the traditional hybrid framework but also consumes less than 20% of baseline tokens, significantly reducing costs while preserving strong alignment with real-world public opinion trends.Our code is available at https://github.com/Jasmine0201/GASim.

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