10 citations · 14 across the 2 of their papers we have counts for
4 papers
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…
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…
Learning fine-grained search space pruning and heuristics for combinatorial optimization
Juho Lauri, Sourav Dutta, Marco Grassia +1
Combinatorial optimization problems arise in a wide range of applications from diverse domains. Many of these problems are NP-hard and designing efficient heuristics for them requi…
Learning Multi-Stage Sparsification for Maximum Clique Enumeration
Marco Grassia, Juho Lauri, Sourav Dutta +1
We propose a multi-stage learning approach for pruning the search space of maximum clique enumeration, a fundamental computationally difficult problem arising in various network an…