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Found 5 papers, 1 papers with code
mGNN: Generalizing the Graph Neural Networks to the Multilayer Case
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.
wsGAT: Weighted and Signed Graph Attention Networks for Link Prediction
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.
Machine learning dismantling and early-warning signals of disintegration in complex systems
From physics to engineering, biology and social science, natural and artificial systems are characterized by interconnected topologies whose features - e. g., heterogeneous connectivity, mesoscale organization, hierarchy - affect their robustness to external perturbations, such as targeted attacks to their units.
Learning fine-grained search space pruning and heuristics for combinatorial optimization
For the classical maximum clique enumeration problem, we show that our framework can prune a large fraction of the input graph (around 99 % of nodes in case of sparse graphs) and still detect almost all of the maximum cliques.
Learning Multi-Stage Sparsification for Maximum Clique Enumeration
Compared to the state-of-the-art heuristics and preprocessing strategies, the advantages of our approach are that (i) it does not require any estimate on the maximum clique size at runtime and (ii) we demonstrate it to be effective also for dense graphs.
