activity
20102025
most citedThe structure and dynamics of multilayer networks

3.1k citations · 3.7k across the 21 of their papers we have counts for

collaborators

22 papers

cs.SI2025

Concentration Within Distribution: Unmasking Bitcoin's Structural Centralization Through Network Science

Myriam Nonaka, F. Javier Marín-Rodríguez, Alexander Jiricny +4

We construct the Bitcoin User Network (BUN) directly from raw blockchain data up to late 2025, which allows us to explore its mesoscopic properties and trace its temporal evolution…

cs.SI2025

Fully personalized PageRank and algebraic methods to distribute a random walker

Gonzalo Contreras-Aso, Regino Criado, Miguel Romance

We present a comprehensive analysis of algebraic methods for controlling the stationary distribution of PageRank-like random walkers. Building upon existing literature, we compile…

cs.SD2025

On the application of Visibility Graphs in the Spectral Domain for Speaker Recognition

Hernan Bocaccio, Sergio Iglesias-Pérez, Miguel Romance +2

In this study, we explore the potential of visibility graphs in the spectral domain for speaker recognition. Adult participants were instructed to record vocalizations of the five…

math-ph2024★ 3 cited

Beyond directed hypergraphs: heterogeneous hypergraphs and spectral centralities

Gonzalo Contreras-Aso, Regino Criado, Miguel Romance

The study of hypergraphs has received a lot of attention over the past few years, however up until recently there has been no interest in systems where higher order interactions ar…

physics.soc-ph2022★ 4 cited

Can the PageRank centrality be manipulated to obtain any desired ranking?

Gonzalo Contreras-Aso, Regino Criado, Miguel Romance

The significance of the PageRank algorithm in shaping the modern Internet cannot be overstated, and its Complex Network theory foundations continue to be a subject of research. In…

physics.soc-ph2022★ 29 cited

Why are there six degrees of separation in a social network?

Ivan Samoylenko, David Aleja, Eva Primo +11

A wealth of evidence shows that real world networks are endowed with the small-world property i.e., that the maximal distance between any two of their nodes scales logarithmically…