  Autonomous navigation is a key skill for assistive and service robots. To be
successful, robots have to minimise the disruption caused to humans while
moving. This implies predicting how people will move and complying with social
conventions. Avoiding disrupting personal spaces, people's paths and
interactions are examples of these social conventions. This paper leverages
Graph Neural Networks to model robot disruption considering the movement of the
humans and the robot so that the model built can be used by path planning
algorithms. Along with the model, this paper presents an evolution of the
dataset SocNav1 which considers the movement of the robot and the humans, and
an updated scenario-to-graph transformation which is tested using different
Graph Neural Network blocks. The model trained achieves close-to-human
performance in the dataset. In addition to its accuracy, the main advantage of
the approach is its scalability in terms of the number of social factors that
can be considered in comparison with handcrafted models.
