6 papers
Distance-Preserving Embeddings in Inhomogeneous Random Graphs
My Le, Luana Ruiz, Souvik Dhara
Graph machine learning provides powerful tools for understanding complex networks and learning meaningful node representations. A central challenge, however, is designing embedding…
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…
Sampling Transferable Graph Neural Networks with Limited Graph Information
Haoyu Wang, Renyuan Ma, Gonzalo Mateos +1
Graph neural networks (GNNs) achieve strong performance on graph learning tasks, but training on large-scale networks remains computationally challenging. Transferability results s…
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…
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…
A Poincaré Inequality and Consistency Results for Signal Sampling on Large Graphs
Thien Le, Luana Ruiz, Stefanie Jegelka
Large-scale graph machine learning is challenging as the complexity of learning models scales with the graph size. Subsampling the graph is a viable alternative, but sampling on gr…