  We consider the problem of learning implicit neural representations (INRs)
for signals on non-Euclidean domains. In the Euclidean case, INRs are trained
on a discrete sampling of a signal over a regular lattice. Here, we assume that
the continuous signal exists on some unknown topological space from which we
sample a discrete graph. In the absence of a coordinate system to identify the
sampled nodes, we propose approximating their location with a spectral
embedding of the graph. This allows us to train INRs without knowing the
underlying continuous domain, which is the case for most graph signals in
nature, while also making the INRs independent of any choice of coordinate
system. We show experiments with our method on various real-world signals on
non-Euclidean domains.
