activity
20192026
most citedCommunity Detection in Large Hypergraphs

72 citations · 159 across the 10 of their papers we have counts for

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Showing cs.SIShow all

6 papers · 1 filter

cs.SI2023★ 5 cited

Message-Passing on Hypergraphs: Detectability, Phase Transitions and Higher-Order Information

Nicolò Ruggeri, Alessandro Lonardi, Caterina De Bacco

Hypergraphs are widely adopted tools to examine systems with higher-order interactions. Despite recent advancements in methods for community detection in these systems, we still la…

cs.SI2023★ 18 cited

Structure and inference in hypergraphs with node attributes

Anna Badalyan, Nicolò Ruggeri, Caterina De Bacco

Many networked datasets with units interacting in groups of two or more, encoded with hypergraphs, are accompanied by extra information about nodes, such as the role of an individu…

cs.SI2023★ 72 cited

Community Detection in Large Hypergraphs

Nicolò Ruggeri, Martina Contisciani, Federico Battiston +1

Hypergraphs, describing networks where interactions take place among any number of units, are a natural tool to model many real-world social and biological systems. In this work we…

cs.SI2022★ 16 cited

A framework to generate hypergraphs with community structure

Nicolò Ruggeri, Federico Battiston, Caterina De Bacco

In recent years hypergraphs have emerged as a powerful tool to study systems with multi-body interactions which cannot be trivially reduced to pairs. While highly structured method…

cs.SI2020★ 1 cited

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…

cs.SI2019★ 7 cited

Sampling on networks: estimating eigenvector centrality on incomplete graphs

Nicolò Ruggeri, Caterina De Bacco

We develop a new sampling method to estimate eigenvector centrality on incomplete networks. Our goal is to estimate this global centrality measure having at disposal a limited amou…