  In many network problems, graphs may change by the addition of nodes, or the
same problem may need to be solved in multiple similar graphs. This generates
inefficiency, as analyses and systems that are not transferable have to be
redesigned. To address this, we consider graphons, which are both limit objects
of convergent graph sequences and random graph models. We define graphon
signals and introduce the Graphon Fourier Transform (WFT), to which the Graph
Fourier Transform (GFT) is shown to converge. This result is demonstrated in
two numerical experiments where, as expected, the GFT converges, hinting to the
possibility of centralizing analysis and design on graphons to leverage
transferability.
