Leadership Detection via Time-Lagged Correlation-Based Network Inference
This addresses leadership detection in fields like animal ecology and swarm robotics, but it is incremental as it builds on existing network and correlation methods.
The study tackled the problem of detecting leadership in collective behavior by proposing a dynamic network inference method using time-lagged correlations across kinematic variables, which outperformed traditional information-theoretic approaches like Transfer Entropy and Time-Lagged Mutual Information in scenarios with limited data, ranking true leaders more consistently.
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.