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Networks are a powerful tool to model complex systems, and the definition ofmany Graph Neural Networks (GNN), Deep Learning algorithms that can handlenetworks, has opened a new way to approach many real-world problems that wouldbe hardly or even untractable. In this paper, we propose mGNN, a frameworkmeant to generalize GNNs to the case of multi-layer networks, i.e., networksthat can model multiple kinds of interactions and relations between nodes. Ourapproach is general (i.e., not task specific) and has the advantage ofextending any type of GNN without any computational overhead. We test theframework into three different tasks (node and network classification, linkprediction) to validate it.

mGNN: Generalizing the Graph Neural Networks to the Multilayer Case