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Found 4 papers, 1 papers with code
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.
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.
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.
Search for overlapped communities by parallel genetic algorithms
In the last decade the broad scope of complex networks has led to a rapid progress.
