Characterizes the distributional mean-field limit of co-evolving latent space networks with feedback, including empirical measures and graphon convergence, via a conditional propagation of chaos result.
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TopoSim integrates network topology into LLM agent simulations via backbone units and heterogeneous influence to cut token use 50-90% while improving fidelity to real-world structures.
Embedding epidemic contact networks via Johnson-Lindenstrauss projections allows estimating the infection source as the node nearest the centroid of infected nodes, achieving meaningful accuracy in simulations on Erdős-Rényi graphs.
Code embeddings combined with the Expressed-Private Opinion model produce trajectories that quantify developer influence and consensus formation across three open-source repositories.
citing papers explorer
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Mean-Field Analysis of Latent Variable Process Models on Dynamically Evolving Graphs with Feedback Effects
Characterizes the distributional mean-field limit of co-evolving latent space networks with feedback, including empirical measures and graphon convergence, via a conditional propagation of chaos result.
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Topology-Aware LLM-Driven Social Simulation: A Unified Framework for Efficient and Realistic Agent Dynamics
TopoSim integrates network topology into LLM agent simulations via backbone units and heterogeneous influence to cut token use 50-90% while improving fidelity to real-world structures.
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Finding Patient Zero via Low-Dimensional Geometric Embeddings
Embedding epidemic contact networks via Johnson-Lindenstrauss projections allows estimating the infection source as the node nearest the centroid of infected nodes, achieving meaningful accuracy in simulations on Erdős-Rényi graphs.
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Social Life of Code: Modeling Evolution through Code Embedding and Opinion Dynamics
Code embeddings combined with the Expressed-Private Opinion model produce trajectories that quantify developer influence and consensus formation across three open-source repositories.