activity
20202025
most citedInference of hyperedges and overlapping communities in hypergraphs

117 citations · 398 across the 11 of their papers we have counts for

collaborators

12 papers

physics.soc-ph2025

Multiscale patterns of migration flows in Austria: regionalization, administrative barriers, and urban-rural divides

Thomas Robiglio, Martina Contisciani, Márton Karsai +1

Migration is central in various societal problems related to socioeconomic development. While much of the existing research has focused on international migration, migration patter…

cs.SI2024

Flexible inference in heterogeneous and attributed multilayer networks

Martina Contisciani, Marius Hobbhahn, Eleanor A. Power +2

Networked datasets can be enriched by different types of information about individual nodes or edges. However, most existing methods for analyzing such datasets struggle to handle…

physics.soc-ph2023★ 40 cited

Hypergraphx: a library for higher-order network analysis

Quintino Francesco Lotito, Martina Contisciani, Caterina De Bacco +6

From social to biological systems, many real-world systems are characterized by higher-order, non-dyadic interactions. Such systems are conveniently described by hypergraphs, where…

cs.SI2023

Anomaly, reciprocity, and community detection in networks

Hadiseh Safdari, Martina Contisciani, Caterina De Bacco

Anomaly detection algorithms are a valuable tool in network science for identifying unusual patterns in a network. These algorithms have numerous practical applications, including…

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★ 117 cited

Inference of hyperedges and overlapping communities in hypergraphs

Martina Contisciani, Federico Battiston, Caterina De Bacco

Hypergraphs, encoding structured interactions among any number of system units, have recently proven a successful tool to describe many real-world biological and social networks. H…

physics.soc-ph2022★ 27 cited

Multilayer patent citation networks: A comprehensive analytical framework for studying explicit technological relationships

Kyle Higham, Martina Contisciani, Caterina De Bacco

The use of patent citation networks as research tools is becoming increasingly commonplace in the field of innovation studies. However, these networks rarely consider the contexts…

cs.SI2021★ 27 cited

Reciprocity, community detection, and link prediction in dynamic networks

Hadiseh Safdari, Martina Contisciani, Caterina De Bacco

Many complex systems change their structure over time, in these cases dynamic networks can provide a richer representation of such phenomena. As a consequence, many inference metho…

cs.SI2021★ 20 cited

Latent Network Models to Account for Noisy, Multiply-Reported Social Network Data

Caterina De Bacco, Martina Contisciani, Jonathan Cardoso-Silva +10

Social network data are often constructed by incorporating reports from multiple individuals. However, it is not obvious how to reconcile discordant responses from individuals. The…

cs.SI2021★ 14 cited

Community detection and reciprocity in networks by jointly modeling pairs of edges

Martina Contisciani, Hadiseh Safdari, Caterina De Bacco

To unravel the driving patterns of networks, the most popular models rely on community detection algorithms. However, these approaches are generally unable to reproduce the structu…

cs.SI2020★ 17 cited

Generative model for reciprocity and community detection in networks

Hadiseh Safdari, Martina Contisciani, Caterina De Bacco

We present a probabilistic generative model and efficient algorithm to model reciprocity in directed networks. Unlike other methods that address this problem such as exponential ra…

cs.SI2020★ 64 cited

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…