  Gaussian Boson Sampling is a non-universal model for quantum computing
inspired by the original formulation of the Boson Sampling problem. Nowadays,
it represents a paradigmatic quantum platform to reach the quantum advantage
regime in a specific computational model. Indeed, thanks to the implementation
in photonics-based processors, the latest Gaussian Boson Sampling experiments
have reached a level of complexity where the quantum apparatus has solved the
task faster than currently up-to-date classical strategies. In addition, recent
studies have identified possible applications beyond the inherent sampling
task. In particular, a direct connection between photon counting of a genuine
Gaussian Boson Sampling device and the number of perfect matchings in a graph
has been established. In this work, we propose to exploit such a connection to
benchmark Gaussian Boson Sampling experiments. We interpret the properties of
the feature vectors of the graph encoded in the device as a signature of
correct sampling from the true input state. Within this framework, two
approaches that exploit the distributions of graph feature vectors and graph
kernels are presented. Our results provide a novel approach to the actual need
for tailored algorithms to benchmark large-scale Gaussian Boson Samplers.
