642 citations · 823 across the 9 of their papers we have counts for
3 papers · 1 filter
mGNN: Generalizing the Graph Neural Networks to the Multilayer Case
Marco Grassia, Manlio De Domenico, Giuseppe Mangioni
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 w…
wsGAT: Weighted and Signed Graph Attention Networks for Link Prediction
Marco Grassia, Giuseppe Mangioni
Graph Neural Networks (GNNs) have been widely used to learn representations on graphs and tackle many real-world problems from a wide range of domains. In this paper we propose wsG…
Machine learning dismantling and early-warning signals of disintegration in complex systems
Marco Grassia, Manlio De Domenico, Giuseppe Mangioni
From physics to engineering, biology and social science, natural and artificial systems are characterized by interconnected topologies whose features - e.g., heterogeneous connecti…