  Real 3-manifold triangulations can be uniquely represented by isomorphism
signatures. Databases of these isomorphism signatures are generated for a
variety of 3-manifolds and knot complements, using SnapPy and Regina, then
these language-like inputs are used to train various machine learning
architectures to differentiate the manifolds, as well as their Dehn surgeries,
via their triangulations. Gradient saliency analysis then extracts key parts of
this language-like encoding scheme from the trained models. The isomorphism
signature databases are taken from the 3-manifolds' Pachner graphs, which are
also generated in bulk for some selected manifolds of focus and for the subset
of the SnapPy orientable cusped census with $<8$ initial tetrahedra. These
Pachner graphs are further analysed through the lens of network science to
identify new structure in the triangulation representation; in particular for
the hyperbolic case, a relation between the length of the shortest geodesic
(systole) and the size of the Pachner graph's ball is observed.
