mGNN: Generalizing the Graph Neural Networks to the Multilayer Case
This work addresses the need for more flexible network modeling in complex systems, though it appears incremental as it builds directly on existing GNN methods.
The authors tackled the problem of generalizing Graph Neural Networks (GNNs) to multi-layer networks, which model multiple interactions between nodes, and proposed mGNN, a framework that extends any GNN type without computational overhead, validated on tasks like node classification, network classification, and link prediction.
Networks are a powerful tool to model complex systems, and the definition of many Graph Neural Networks (GNN), Deep Learning algorithms that can handle networks, has opened a new way to approach many real-world problems that would be hardly or even untractable. In this paper, we propose mGNN, a framework meant to generalize GNNs to the case of multi-layer networks, i.e., networks that can model multiple kinds of interactions and relations between nodes. Our approach is general (i.e., not task specific) and has the advantage of extending any type of GNN without any computational overhead. We test the framework into three different tasks (node and network classification, link prediction) to validate it.