Living organisms need to solve rooting problems by selecting sets of paths linking several points of interests. Path selection processes are ubiquitous and ecologically valuable in plants, fungi, ameboids, and in animals like central-place foragers. However, path selection can be difficult to study, as some selection cues are not always visible (e.g. pheromones in insects). In this paper we present a method to study dynamical path selection behaviour solely from time-lapse images. We demonstrate how it can be applied to study freely roaming termites behaviour. Our method reconstructs the network of all paths ever taken and quantifies the amount of individuals for each edge at each time-step. The path selection behaviour can then be studied with null models. One can test for individual rules by simulating agents or running differential equations models in the network. Behavioural hypotheses can then be tested by comparing with observed networks’ properties, at any network scale and any time-step. We used this method to reconstruct freely roaming termites networks. We used differential equations to model the diffusion of termites in the network solely based on arbitrary turning preferences. Surprisingly, the common feature of animals to follow borders (thigmotaxy) emerged from this simple rule. However, our model lacked an amplifying mechanism to reproduce the intensity of termites’ path selection process. This was expected, as pheromones were not implemented in the model. This method could be used on any time-lapse data of different species, and authors are open for collaboration.