Detecting evidence of organization in animals is essential in computational biology for several reasons, as it helps researchers better understand the biology of animals and develop practical applications. However, animals can interact in many ways, including predatory, competitive, mutualistic, and social, and these interactions are not always easily observable. Thus, this article focuses on solving the Network Structure Inference (NSI) challenge in groups of animals. We present two novel methods for detecting network structures from trajectories. The first approach involves evaluating graph entropy, while the second utilizes cluster quality indexes. To assess the effectiveness of these new approaches, we conducted experiments using four scenario simulations inspired by the animal kingdom, implemented on the NetLogo platform: Ants, Wolf Sheep Predation, Flocking, and Ant Adaptation. Additionally, we compare the outcomes with those of a previously proposed method in the literature, applying all techniques to simulations within the NetLogo platform. The results indicate that our approaches offer enhanced clarity in identifying organizational or network inferences within simulated scenarios inspired by the animal kingdom.