From the 1 of 10 linked papers with an AI index.
10 papers
Train Small, Deploy Large: Zero-Shot GNN Transfer Through Geometric Renormalization
Robert Jankowski, Pedro Almagro-Blanco, Marián Boguñá +2
The paper proposes training graph neural networks on geometrically renormalized, coarse‑grained versions of a graph and then directly applying the learned weights to the original f…
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…
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…
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…
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…
Mapping bipartite networks into multidimensional hyperbolic spaces
Robert Jankowski, Roya Aliakbarisani, M. Ãngeles 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…