3 citations · 3 across the 6 of their papers we have counts for
6 papers
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…
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…
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…
Feature-enriched hyperbolic network geometry
Roya Aliakbarisani, M. Ángeles Serrano, Marián Boguñá
Graph-structured data provide a comprehensive description of complex systems, encompassing not only the interactions among nodes but also the intrinsic features that characterize t…
Predictability of missing links in complex networks
Guillermo García-Pérez, Roya Aliakbarisani, Abdorasoul Ghasemi +1
Predicting missing links in real networks is an important problem in network science to which considerable efforts have been devoted, giving as a result a vast plethora of link pre…