Quantification of collective behaviour via causality analysis

Terms such as leader, follower, and oppressed sound equally well in the description of a pack of wolves, a street protest crowd, or a business team and have very similar meanings. This indicates the presence of some general law or structure that governs collective behaviour. To reveal this, we selected the most common parameter for all levels of the organization – motion. A causality analysis of distance correlations was performed to obtain follow-up networks that show who follows whom and how strongly. These networks characterize an observed system in general and work as an automation bridge between the biological experiment and the broad field of network analysis. The proposed method was tested on a school of aquarium fish. The pattern observed in the network can be easily interpreted and are in agreement with expected behaviour. Here we provide a Matlab code and a resulted video of the fish in aquarium and the follow-up networks., , , # Quantification of collective behaviour via causality analysis

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The image data was collected and processed by a procedure described in the related manuscript.

The **video** demonstrates the temporal changes in the follow-up networks, specifically the configurations of the fish schools, throughout the course of the experiment. It includes: the top view (upper left), bottom view (upper right), and front view (lower left) of the fish school within the aquarium, as well as the follow-up network (lower right). These various angles provide a comprehensive visualization of the dynamic changes in fish school behavior and network formation over time.

The Matlab code is for the computation of the follow-up networks for time-lapse images of a fish school. The README files for each computation step of the trajectory optimization are included in the code.zip.

For more details contact: [[e-mail]](mailto:[e-mail])