1 citations · 2 across the 11 of their papers we have counts for
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Hyperbolic Graph Embedders for Link Prediction and Topology Reconstruction
Robert Jankowski, Maksim Kitsak, Dorota Celińska-Kopczyńska
Hyperbolic embeddings provide compact geometric representations of complex networks in hyperbolic spaces, but systematic comparisons of methods developed in machine learning, netwo…
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…
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…
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…