标题： 基于时滞相关性的领导力检测网络推断 显示英文标题

标题： Leadership Detection via Time-Lagged Correlation-Based Network Inference

摘要： 理解集体行为中的领导力动态是动物生态学、群体机器人学和智能交通领域的一个关键挑战。传统的信息论方法，包括转移熵（TE）和时滞互信息（TLMI），已被广泛用于推断领导者-跟随者关系，但由于它们依赖于稳健的概率估计，在噪声数据或短时数据集上面临关键限制。本研究提出了一种基于动态网络推断的方法，利用多个运动学变量（速度、加速度和方向）之间的时滞相关性。我们的方法在时间上构建有向影响图，从而能够在不需要大量数据或参数敏感的离散化的情况下识别领导模式。我们通过两个NetLogo中的多智能体模拟验证了该方法：一个具有知情领导者的修改后的Vicsek模型，以及一个包含协调和独立狼群的捕食者-猎物模型。实验结果表明，在空间时间观测有限的场景中，基于网络的方法优于TE和TLMI，在更一致地将真实领导者排在影响度量顶部方面表现更好。 显示英文摘要

摘要： 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.