Deciphering the social interactions that govern collective behavior in animal
societies has greatly benefited from advancements in modern computing.
Computational models diverge into two kinds of approaches: analytical models
and machine learning models. This work introduces a deep learning model for
social interactions in the fish species Hemigrammus rhodostomus, and compares
its results to experiments and to the results of a state-of-the-art analytical
model. To that end, we propose a systematic methodology to assess the
faithfulness of a model, based on the introduction of a set of stringent
observables. We demonstrate that machine learning models of social interactions
can directly compete against their analytical counterparts. Moreover, this work
demonstrates the need for consistent validation across different timescales and
highlights which design aspects critically enables our deep learning approach
to capture both short- and long-term dynamics. We also show that this approach
is scalable to other fish species