Leadership Detection via Time-Lagged Correlation-Based Network Inference

TL;DR

This paper introduces a network inference method based on time-lagged correlations to detect leadership in collective behavior, especially effective with limited or noisy data, outperforming traditional information-theoretic approaches.

Contribution

The study presents a novel dynamic network inference approach using time-lagged correlations across multiple kinematic variables for leadership detection.

Findings

Outperforms Transfer Entropy and TLMI in noisy or short datasets

Effectively identifies true leaders in multi-agent simulations

Provides reliable influence metrics with limited data

Abstract

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.