  The field of Graph Signal Processing (GSP) has proposed tools to generalize
harmonic analysis to complex domains represented through graphs. Among these
tools are translations, which are required to define many others. Most works
propose to define translations using solely the graph structure (i.e. edges).
Such a problem is ill-posed in general as a graph conveys information about
neighborhood but not about directions. In this paper, we propose to infer
translations as edge-constrained operations that make a supervised
classification problem invariant using a deep learning framework. As such, our
methodology uses both the graph structure and labeled signals to infer
translations. We perform experiments with regular 2D images and abstract
hyperlink networks to show the effectiveness of the proposed methodology in
inferring meaningful translations for signals supported on graphs.
