147 citations · 466 across the 22 of their papers we have counts for
22 papers
Latent geometry organizes higher-order interactions
Berné L. Nortier, Jasper van der Kolk, Robert Jankowski +4
Higher-order structures offer a natural representation of complex systems that involve interactions between groups of different sizes. A widespread feature of their higher-order st…
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…
Motif-based filtrations for persistent homology: A framework for graph isomorphism and property prediction
Meritxell Vila-Miñana, Robert Jankowski, Aina Ferrà Marcús +3
Determining whether two graphs are isomorphic is a fundamental problem with practical applications in areas such as molecular chemistry or social network analysis, yet it remains a…
Statistical Mechanics of Random Hyperbolic Graphs within the Fermionic Maximum-Entropy Framework
M. Ángeles Serrano
The intricate relations between elements in natural and human-made systems sustain the complex processes that shape our world, forming multiscale networks of interactions. These ne…
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…
Chordless cycle filtrations for dimensionality detection in complex networks via topological data analysis
Aina Ferrà Marcús, Robert Jankowski, Meritxell Vila Miñana +2
Many complex networks, ranging from social to biological systems, exhibit structural patterns consistent with an underlying hyperbolic geometry. Revealing the dimensionality of thi…
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…
Quantum Key Distribution over Complex Networks
Luca Mariani, Raja Yehia, Carlos Pascual-García +4
There exist several initiatives worldwide to deploy quantum key distribution (QKD) over existing fibre networks and achieve quantum-safe security at large scales. To understand the…
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…
Statistical mechanics of directed networks
Marián Boguñá, M. Ángeles Serrano
Directed networks are essential for representing complex systems, capturing the asymmetry of interactions in fields such as neuroscience, transportation, and social networks. Direc…
Hyperbolic Benchmarking Unveils Network Topology-Feature Relationship in GNN Performance
Roya Aliakbarisani, Robert Jankowski, M. Ángeles Serrano +1
Graph Neural Networks (GNNs) have excelled in predicting graph properties in various applications ranging from identifying trends in social networks to drug discovery and malware d…
Renormalization of networks with weak geometric coupling
Jasper van der Kolk, Marián Boguñá, M. Ángeles Serrano
The Renormalization Group is crucial for understanding systems across scales, including complex networks. Renormalizing networks via network geometry, a framework in which their to…
Feature-aware ultra-low dimensional reduction of real networks
Robert Jankowski, Pegah Hozhabrierdi, Marián Boguñá +1
In existing models and embedding methods of networked systems, node features describing their qualities are usually overlooked in favor of focusing solely on node connectivity. Thi…
Random graphs and real networks with weak geometric coupling
J. van der Kolk, M. Á. Serrano, M. Boguñá
Geometry can be used to explain many properties commonly observed in real networks. It is therefore often assumed that real networks, especially those with high average local clust…
The D-Mercator method for the multidimensional hyperbolic embedding of real networks
Robert Jankowski, Antoine Allard, Marián Boguñá +1
One of the pillars of the geometric approach to networks has been the development of model-based mapping tools that embed real networks in its latent geometry. In particular, the t…
Soft communities in similarity space
Guillermo García-Pérez, M. Ángeles Serrano, Marián Boguñá
The model has been a central geometric model in the development of the field of network geometry. It has been mainly studied in its homogeneous regime, in which angu…
Multiscale unfolding of real networks by geometric renormalization
Guillermo García-Pérez, Marián Boguñá, M. Ángeles Serrano
Multiple scales coexist in complex networks. However, the small world property makes them strongly entangled. This turns the elucidation of length scales and symmetries a defiant c…
Geometric correlations mitigate the extreme vulnerability of multiplex networks against targeted attacks
Kaj-Kolja Kleineberg, Lubos Buzna, Fragkiskos Papadopoulos +2
We show that real multiplex networks are unexpectedly robust against targeted attacks on high degree nodes, and that hidden interlayer geometric correlations predict this robustnes…
Hidden geometric correlations in real multiplex networks
Kaj-Kolja Kleineberg, Marian Boguna, M. Angeles Serrano +1
Real networks often form interacting parts of larger and more complex systems. Examples can be found in different domains, ranging from the Internet to structural and functional br…
The hidden geometry of weighted complex networks
Antoine Allard, M. Ángeles Serrano, Guillermo García-Pérez +1
The topology of many real complex networks has been conjectured to be embedded in hidden metric spaces, where distances between nodes encode their likelihood of being connected. Be…