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stat.ML2025
A Spectral Framework for Graph Neural Operators: Convergence Guarantees and Tradeoffs
Roxanne Holden, Luana Ruiz
Graphons, as limits of graph sequences, provide an operator-theoretic framework for analyzing the asymptotic behavior of graph neural operators. Spectral convergence of sampled gra…
stat.ML2025
Landmark-Based Node Representations for Shortest Path Distance Approximations in Random Graphs
My Le, Luana Ruiz, Souvik Dhara
Learning node representations is a fundamental problem in graph machine learning. While existing embedding methods effectively preserve local similarity measures, they often fail t…
cs.LG2025
Subsampling Graphs with GNN Performance Guarantees
Mika Sarkin Jain, Stefanie Jegelka, Ishani Karmarkar +2
How can we subsample graph data so that a graph neural network (GNN) trained on the subsample achieves performance comparable to training on the full dataset? This question is of f…