  Graphs are nowadays ubiquitous in the fields of signal processing and machine
learning. As a tool used to express relationships between objects, graphs can
be deployed to various ends: I) clustering of vertices, II) semi-supervised
classification of vertices, III) supervised classification of graph signals,
and IV) denoising of graph signals. However, in many practical cases graphs are
not explicitly available and must therefore be inferred from data. Validation
is a challenging endeavor that naturally depends on the downstream task for
which the graph is learnt. Accordingly, it has often been difficult to compare
the efficacy of different algorithms. In this work, we introduce several
ease-to-use and publicly released benchmarks specifically designed to reveal
the relative merits and limitations of graph inference methods. We also
contrast some of the most prominent techniques in the literature.
