18 citations · 69 across the 60 of their papers we have counts for
5 papers · 1 filter
Resilient Graph Neural Networks: A Coupled Dynamical Systems Approach
Moshe Eliasof, Davide Murari, Ferdia Sherry +1
Graph Neural Networks (GNNs) have established themselves as a key component in addressing diverse graph-based tasks. Despite their notable successes, GNNs remain susceptible to inp…
Efficient Subgraph GNNs by Learning Effective Selection Policies
Beatrice Bevilacqua, Moshe Eliasof, Eli Meirom +2
Subgraph GNNs are provably expressive neural architectures that learn graph representations from sets of subgraphs. Unfortunately, their applicability is hampered by the computatio…
Feature Transportation Improves Graph Neural Networks
Moshe Eliasof, Eldad Haber, Eran Treister
Graph neural networks (GNNs) have shown remarkable success in learning representations for graph-structured data. However, GNNs still face challenges in modeling complex phenomena…
DRIP: Deep Regularizers for Inverse Problems
Moshe Eliasof, Eldad Haber, Eran Treister
In this paper we consider inverse problems that are mathematically ill-posed. That is, given some (noisy) data, there is more than one solution that approximately fits the data. In…
Graph Positional Encoding via Random Feature Propagation
Moshe Eliasof, Fabrizio Frasca, Beatrice Bevilacqua +3
Two main families of node feature augmentation schemes have been explored for enhancing GNNs: random features and spectral positional encoding. Surprisingly, however, there is stil…