用时间滞后相关性构建动态网络，高效识别群体中的领导者。
Leadership Detection via Time-Lagged Correlation-Based Network Inference
- 基于速度、加速度和方向的时滞相关性构建有向影响图。
- 在数据量少或噪声大的情况下，准确率高于传统熵方法。
- 适用于动物行为、机器人集群等缺乏大量数据的场景。
理解集体行为中的领导力动态是动物生态学、群体机器人和智能交通领域的关键挑战。传统信息论方法（如转移熵TE和时滞互信息TLMI）虽广泛用于推断领导者-追随者关系，但在噪声大或持续时间短的数据集上因依赖精确的概率估计而表现受限。本文提出一种基于多运动变量（速度、加速度、方向）时滞相关性的动态网络推断方法，构建随时间演化的有向影响图，无需大量数据或敏感参数离散化即可识别领导模式。通过NetLogo平台的两个多智能体仿真验证：一是含知情领导者修正版Vicsek模型，二是包含协同与独立狼群的捕食-逃避模型。实验表明，在时空观测有限的场景中，该方法在识别真实领导者方面优于TE与TLMI，其影响力排序更稳定可靠。
原文摘要 · Abstract (English)
Understanding leadership dynamics in collective behavior is a key challenge in animal ecology, swarm robotics, and intelligent transportation. Traditional information-theoretic approaches, including Transfer Entropy (TE) and Time-Lagged Mutual Information (TLMI), have been widely used to infer leader-follower relationships but face critical limitations in noisy or short-duration datasets due to their reliance on robust probability estimations. This study proposes a method based on dynamic network inference using time-lagged correlations across multiple kinematic variables: velocity, acceleration, and direction. Our approach constructs directed influence graphs over time, enabling the identification of leadership patterns without the need for large volumes of data or parameter-sensitive discretization. We validate our method through two multi-agent simulations in NetLogo: a modified Vicsek model with informed leaders and a predator-prey model featuring coordinated and independent wolf groups. Experimental results demonstrate that the network-based method outperforms TE and TLMI in scenarios with limited spatiotemporal observations, ranking true leaders at the top of influence metrics more consistently than TE and TLMI.
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