70 citations · 110 across the 33 of their papers we have counts for
4 papers · 1 filter
Equivariant Machine Learning on Graphs with Nonlinear Spectral Filters
Ya-Wei Eileen Lin, Ronen Talmon, Ron Levie
Equivariant machine learning is an approach for designing deep learning models that respect the symmetries of the problem, with the aim of reducing model complexity and improving g…
Learning on Large Graphs using Intersecting Communities
Ben Finkelshtein, İsmail İlkan Ceylan, Michael Bronstein +1
Message Passing Neural Networks (MPNNs) are a staple of graph machine learning. MPNNs iteratively update each node's representation in an input graph by aggregating messages from t…
Generalization Bounds for Message Passing Networks on Mixture of Graphons
Sohir Maskey, Gitta Kutyniok, Ron Levie
We study the generalization capabilities of Message Passing Neural Networks (MPNNs), a prevalent class of Graph Neural Networks (GNN). We derive generalization bounds specifically…
Future Directions in the Theory of Graph Machine Learning
Christopher Morris, Fabrizio Frasca, Nadav Dym +7
Machine learning on graphs, especially using graph neural networks (GNNs), has seen a surge in interest due to the wide availability of graph data across a broad spectrum of discip…