642 citations · 1.6k across the 20 of their papers we have counts for
22 papers
ProtoGuide: Prototype-Driven Guidance for Class-Conditional Graph Generation
Salvatore Romano, Marco Grassia, Pietro Liò +1
Discrete diffusion models are a prominent family for graph generation, but standard class-conditional mechanisms embed the class signal in the denoiser during training, tying the c…
Brain-Grasp: Graph-based Saliency Priors for Improved fMRI-based Visual Brain Decoding
Mohammad Moradi, Morteza Moradi, Marco Grassia +1
Recent progress in brain-guided image generation has improved the quality of fMRI-based reconstructions; however, fundamental challenges remain in preserving object-level structure…
Beyond MMD: Evaluating Graph Generative Models with Geometric Deep Learning
Salvatore Romano, Marco Grassia, Giuseppe Mangioni
Graph generation is a crucial task in many fields, including network science and bioinformatics, as it enables the creation of synthetic graphs that mimic the properties of real-wo…
Robustness and resilience of complex networks
Oriol Artime, Marco Grassia, Manlio De Domenico +5
Complex networks are ubiquitous: a cell, the human brain, a group of people and the Internet are all examples of interconnected many-body systems characterized by macroscopic prope…
The rise and fall of countries in the global value chains
Luiz G. A. Alves, Giuseppe Mangioni, Francisco A. Rodrigues +2
Countries participate in global value chains by engaging in backward and forward transactions connecting multiple geographically dispersed production stages. Inspired by network th…
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…