4 papers · 1 filter
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…
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…
A Local Graph Limits Perspective on Sampling-Based GNNs
Yeganeh Alimohammadi, Luana Ruiz, Amin Saberi
We propose a theoretical framework for training Graph Neural Networks (GNNs) on large input graphs via training on small, fixed-size sampled subgraphs. This framework is applicable…