7 papers
How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?
Michela Lapenna, Caterina De Bacco
Graphs are a powerful data structure for representing relational data and are widely used to describe complex real-world systems. Probabilistic Graphical Models (PGMs) and Graph Ne…
Broad Spectrum Structure Discovery in Large-Scale Higher-Order Networks
John Hood, Caterina De Bacco, Aaron Schein
Complex systems are often driven by higher-order interactions among multiple units, naturally represented as hypergraphs. Understanding dependency structures within these hypergrap…
Principled network extraction from images
Diego Baptista, Caterina De Bacco
Images of natural systems may represent patterns of network-like structure, which could reveal important information about the topological properties of the underlying subject. How…
Designing optimal networks for multi-commodity transport problem
Alessandro Lonardi, Enrico Facca, Mario Putti +1
Designing and optimizing different flows in networks is a relevant problem in many contexts. While a number of methods have been proposed in the physics and optimal transport liter…
Community detection with node attributes in multilayer networks
Martina Contisciani, Eleanor Power, Caterina De Bacco
Community detection in networks is commonly performed using information about interactions between nodes. Recent advances have been made to incorporate multiple types of interactio…
Sampling on networks: estimating spectral centrality measures and their impact in evaluating other relevant network measures
Nicolò Ruggeri, Caterina De Bacco
We perform an extensive analysis of how sampling impacts the estimate of several relevant network measures. In particular, we focus on how a sampling strategy optimized to recover…