Sampling on Networks: Estimating Eigenvector Centrality on Incomplete Networks. - Nicolò Ruggeri; Caterina De Bacco (COMPLEX NETWORKS (1), 2019)

Sampling on networks: estimating eigenvector centrality on incomplete graphs. - Nicolò Ruggeri; Caterina De Bacco (CoRR, 2019)

Sampling on networks: estimating spectral centrality measures and their impact in evaluating other relevant network measures. - Nicolò Ruggeri; Caterina De Bacco (Appl. Netw. Sci., 2020)

Fast rates for noisy interpolation require rethinking the effect of inductive bias. - Konstantin Donhauser; Nicolò Ruggeri; Stefan Stojanovic; Fanny Yang (ICML, 2022)

Fast rates for noisy interpolation require rethinking the effects of inductive bias. - Konstantin Donhauser; Nicolò Ruggeri; Stefan Stojanovic; Fanny Yang (CoRR, 2022)

Provable concept learning for interpretable predictions using variational inference. - Armeen Taeb; Nicolò Ruggeri; Carina Schnuck; Fanny Yang (CoRR, 2022)

A Principled, Flexible and Efficient Framework for Hypergraph Benchmarking. - Nicolò Ruggeri; Federico Battiston; Caterina De Bacco (CoRR, 2022)

Generalized Inference of Mesoscale Structures in Higher-order Networks. - Nicolò Ruggeri; Martina Contisciani; Federico Battiston; Caterina De Bacco (CoRR, 2023)

Hypergraphs with node attributes: structure and inference. - Anna Badalyan; Nicolò Ruggeri; Caterina De Bacco (CoRR, 2023)

Message-Passing on Hypergraphs: Detectability, Phase Transitions and Higher-Order Information. - Nicolò Ruggeri; Alessandro Lonardi; Caterina De Bacco (CoRR, 2023)

Hypergraphx: a library for higher-order network analysis. - Quintino Francesco Lotito; Martina Contisciani; Caterina De Bacco; Leonardo Di Gaetano; Luca Gallo; Alberto Montresor; Federico Musciotto; Nicolò Ruggeri; Federico Battiston (J. Complex Networks, 2023)

Probabilistic Models and Approximate Inference Methods for Structured Data. - Nicolò Ruggeri (2024)