5 citations · 5 across the 4 of their papers we have counts for
4 papers
Graph Representational Learning: When Does More Expressivity Hurt Generalization?
Sohir Maskey, Raffaele Paolino, Fabian Jogl +2
Graph Neural Networks (GNNs) are powerful tools for learning on structured data, yet the relationship between their expressivity and predictive performance remains unclear. We intr…
Weisfeiler and Leman Go Loopy: A New Hierarchy for Graph Representational Learning
Raffaele Paolino, Sohir Maskey, Pascal Welke +1
We introduce -loopy Weisfeiler-Leman (-WL), a novel hierarchy of graph isomorphism tests and a corresponding GNN framework, -MPNN, that can count cycles up…
A Fractional Graph Laplacian Approach to Oversmoothing
Sohir Maskey, Raffaele Paolino, Aras Bacho +1
Graph neural networks (GNNs) have shown state-of-the-art performances in various applications. However, GNNs often struggle to capture long-range dependencies in graphs due to over…
Unveiling the Sampling Density in Non-Uniform Geometric Graphs
Raffaele Paolino, Aleksandar Bojchevski, Stephan Günnemann +2
A powerful framework for studying graphs is to consider them as geometric graphs: nodes are randomly sampled from an underlying metric space, and any pair of nodes is connected if…