2 citations · 2 across the 7 of their papers we have counts for
3 papers · 1 filter
Graph Homomorphism Distortion: A structural (pseudo)-metric for attributed graphs
Martin Carrasco, Olga Zaghen, Kavir Sumaraj +2
A large driver of the complexity of graph learning is the interplay between structure and features. When analyzing the expressivity of graph neural networks, however, existing appr…
On the Rademacher Complexity of Graph Neural Networks: Unifying Expressivity and Geometry
Martin Carrasco, Caio F. Deberaldini Netto, Vahan A. Martirosyan +2
Understanding the interplay between generalization, expressivity, and the geometry of the input space is a central challenge in graph learning. The expressivity of Graph Neural Net…
HOPSE: Scalable Higher-Order Positional and Structural Encoder for Combinatorial Representations
Guillermo Bernárdez, Marco Montagna, Louis Van Langendonck +7
While Graph Neural Networks (GNNs) have proven highly effective at modeling relational data, pairwise connections cannot fully capture multi-way relationships naturally present in…