1 citations · 3 across the 8 of their papers we have counts for
9 papers
Sampling biases in sprinkled causal sets
Marián Boguñá, Dmitri Krioukov
Poisson sprinkling provides a Lorentz-invariant way of generating causal sets from a continuum spacetime, but numerical work necessarily restricts the sprinkling to a finite region…
Train Small, Deploy Large: Zero-Shot GNN Transfer Through Geometric Renormalization
Robert Jankowski, Pedro Almagro-Blanco, Marián Boguñá +2
Graph neural networks (GNNs) can operate on large graphs but become infrastructure-sensitive at the scale of millions of nodes and typically require scalable training techniques fo…
Extracting the geometric backbone of bipartite networks
Lucía S. Ramírez, Roya Aliakbarisani, M. Ángeles Serrano +1
Real bipartite networks combine degree-constrained random mixing with structured, locality-like rules. We introduce a statistical filter that benchmarks node-level bipartite cluste…
Trade-offs between structural richness and perceptual robustness in music network representations
Lluc Bono Rosselló, Robert Jankowski, Hugues Bersini +2
Music is a structured and perceptually rich sequence of sounds in time, whose perception is shaped by the interplay of expectation and uncertainty about what comes next. Yet the un…
Task complexity shapes internal representations and robustness in neural networks
Robert Jankowski, Filippo Radicchi, M. Ángeles Serrano +2
Neural networks excel across a wide range of tasks, yet remain black boxes. In particular, how their internal representations are shaped by the complexity of the input data and the…
Local d'Alembertian for causal sets
Marián Boguñá, Dmitri Krioukov
Causal set theory is an intrinsically nonlocal approach to quantum gravity, inheriting its nonlocality from Lorentzian nonlocality. This nonlocality causes problems in defining dif…
Multiplexity amplifies geometry in networks
Jasper van der Kolk, Dmitri Krioukov, Marián Boguñá +1
Many real-world network are multilayer, with nontrivial correlations across layers. Here we show that these correlations amplify geometry in networks. We focus on mutual clustering…
Clustering Does Not Always Imply Latent Geometry
Roya Aliakbarisani, Marián Boguñá, M. Ángeles Serrano
The latent space approach to complex networks has revealed fundamental principles and symmetries, enabling geometric methods. However, the conditions under which network topology i…
Mapping bipartite networks into multidimensional hyperbolic spaces
Robert Jankowski, Roya Aliakbarisani, M. Ángeles Serrano +1
Bipartite networks appear in many real-world contexts, linking entities across two distinct sets. They are often analyzed via one-mode projections, but such projections can introdu…