  Data are often represented as graphs. Many common tasks in data science are
based on distances between entities. While some data science methodologies
natively take graphs as their input, there are many more that take their input
in vectorial form. In this survey we discuss the fundamental problem of mapping
graphs to vectors, and its relation with mathematical programming. We discuss
applications, solution methods, dimensional reduction techniques and some of
their limits. We then present an application of some of these ideas to neural
networks, showing that distance geometry techniques can give competitive
performance with respect to more traditional graph-to-vector mappings.
