  Multiplex networks are collections of networks with identical nodes but
distinct layers of edges. They are genuine representations for a large variety
of real systems whose elements interact in multiple fashions or flavors.
However, multiplex networks are not always simple to observe in the real world;
often, only partial information on the layer structure of the networks is
available, whereas the remaining information is in the form of aggregated,
single-layer networks. Recent works have proposed solutions to the problem of
reconstructing the hidden multiplexity of single-layer networks using tools
proper of network science. Here, we develop a machine learning framework that
takes advantage of graph embeddings, i.e., representations of networks in
geometric space. We validate the framework in systematic experiments aimed at
the reconstruction of synthetic and real-world multiplex networks, providing
evidence that our proposed framework not only accomplishes its intended task,
but often outperforms existing reconstruction techniques.
