Analysis of Pairwise Interactions in a Maximum Likelihood Sense to Identify Leaders in a Group

Violet Mwaffo, Sachit Butail, Maurizio Porfiri

Research output: Contribution to journalArticlepeer-review

Abstract

Collective motion in animal groups manifests itself in the form of highly coordinated maneuvers determined by local interactions among individuals. A particularly critical question in understanding the mechanisms behind such interactions is to detect and classify leader–follower relationships within the group. In the technical literature of coupled dynamical systems, several methods have been proposed to reconstruct interaction networks, including linear correlation analysis, transfer entropy, and event synchronization. While these analyses have been helpful in reconstructing network models from neuroscience to public health, rules on the most appropriate method to use for a specific dataset are lacking. Here, we demonstrate the possibility of detecting leaders in a group from raw positional data in a model-free approach that combines multiple methods in a maximum likelihood sense. We test our framework on synthetic data of groups of self-propelled Vicsek particles, where a single agent acts as a leader and both the size of the interaction region and the level of inherent noise are systematically varied. To assess the feasibility of detecting leaders in real-world applications, we study a synthetic dataset of fish shoaling, generated by using a recent data-driven model for social behavior, and an experimental dataset of pharmacologically treated zebrafish. Not only does our approach offer a robust strategy to detect leaders in synthetic data but it also allows for exploring the role of psychoactive compounds on leader–follower relationships.

Original languageEnglish (US)
Article number35
JournalFrontiers in Robotics and AI
Volume4
DOIs
StatePublished - Jul 31 2017

Keywords

  • ROC
  • classification
  • event synchronization
  • network
  • self-propelled particles
  • transfer entropy
  • zebrafish

ASJC Scopus subject areas

  • Computer Science Applications
  • Artificial Intelligence

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