4 papers · 1 filter
GraCo: A Unified Toolkit of Graph Coarsening Algorithms and Metrics
Antonin Joly
Graph coarsening aims to reduce a graph to a smaller one that preserves chosen properties of the original. It is increasingly used to scale graph learning to large graphs, as a non…
CoRe-GNN: Multilevel Message passing on Coarsened graphs
Antonin Joly, Nicolas Keriven, Aline Roumy
Training Graph Neural Networks on large graphs is challenged by the memory cost of storing all node representations across layers. We show that several existing scalable approaches…
Taxonomy of reduction matrices for Graph Coarsening
Antonin Joly, Nicolas Keriven, Aline Roumy
Graph coarsening aims to diminish the size of a graph to lighten its memory footprint, and has numerous applications in graph signal processing and machine learning. It is usually…
Graph Coarsening with Message-Passing Guarantees
Antonin Joly, Nicolas Keriven
Graph coarsening aims to reduce the size of a large graph while preserving some of its key properties, which has been used in many applications to reduce computational load and mem…