2 citations · 5 across the 12 of their papers we have counts for
4 papers · 1 filter
On the Utilization of Unique Node Identifiers in Graph Neural Networks
Maya Bechler-Speicher, Moshe Eliasof, Carola-Bibiane Schönlieb +2
Graph Neural Networks have inherent representational limitations due to their message-passing structure. Recent work has suggested that these limitations can be overcome by using u…
A General Recipe for Contractive Graph Neural Networks -- Technical Report
Maya Bechler-Speicher, Moshe Eliasof
Graph Neural Networks (GNNs) have gained significant popularity for learning representations of graph-structured data due to their expressive power and scalability. However, despit…
Cayley Graph Propagation
JJ Wilson, Maya Bechler-Speicher, Petar Veličković
In spite of the plethora of success stories with graph neural networks (GNNs) on modelling graph-structured data, they are notoriously vulnerable to over-squashing, whereby tasks n…
The Interpretable and Effective Graph Neural Additive Networks
Maya Bechler-Speicher, Amir Globerson, Ran Gilad-Bachrach
Graph Neural Networks (GNNs) have emerged as the predominant approach for learning over graph-structured data. However, most GNNs operate as black-box models and require post-hoc e…