Tools and algorithms

Jan 1, 2022
Image credit: Unsplash

In the last decades, advances in technology have generated an unprecedented amount of relational data across a variety of domains, which made network science theory a crucial tool to investigate biological, technological and social systems. A pivotal role in advancing network science has been played by the development of efficient algorithms and computational tools to analyze networked data.

The goal of this project is to develop computational frameworks for the analysis of networks and their generalizations.

Complex networks Multiplex Hypergraphs Simplicial complexes Python
Luca Gallo
Luca Gallo
Postdoctoral research fellow

My research focuses on applying network science to studying the structure and dynamics of social systems.

Publications

MPXGAT: An Attention based Deep Learning Model for Multiplex Graphs Embedding
Graph representation learning has rapidly emerged as a pivotal field of study. Despite its growing popularity, the majority of research …
Marco Bongiovanni, Luca Gallo, Roberto Grasso, Alfredo Pulvirenti
Cite Project Preprint GitHub
MPXGAT: An Attention based Deep Learning Model for Multiplex Graphs Embedding
Hypergraphx: a library for higher-order network analysis
From social to biological systems, many real-world systems are characterized by higher-order, non-dyadic interactions. Such systems are …
Quintino Francesco Lotito, Martina Contisciani, Caterina De Bacco, Leonardo Di Gaetano, Luca Gallo, Alberto Montresor, Federico Musciotto, Nicolò Ruggeri, Federico Battiston
Cite Project Preprint DOI GitHub
Hypergraphx: a library for higher-order network analysis
MultiplexSAGE: A Multiplex Embedding Algorithm for Inter-Layer Link Prediction
Research on graph representation learning has received great attention in recent years. However, most of the studies so far have …
Luca Gallo, Vito Latora, Alfredo Pulvirenti
Cite Project Preprint DOI GitHub
MultiplexSAGE: A Multiplex Embedding Algorithm for Inter-Layer Link Prediction