arXiv:2601.22943v2 Announce Type: replace Abstract: Graph coarsening reduces the size of a graph while preserving certain properties. Most existing methods preserve either spectral or spatial characteristics. Recent research shows that topology-preserving coarsening methods maintain GNN performance on coarsened graphs but suffer from exponential time complexity. To address these problems, we propose Scalable Topology-Preserving Graph Coarsening (STPGC) by introducing the concepts of graph strong collapse and graph edge collapse extended from algebraic topology. STPGC comprises three new algori
Ongoing research into graph neural networks (GNNs) continuously pushes for improved efficiency and scalability, making this a natural progression in addressing computational bottlenecks.
This development could significantly enhance the practical deployment of GNNs in complex, large-scale applications by making them computationally viable.
The proposed STPGC algorithms address the historical trade-off between topology preservation and computational complexity in graph coarsening, potentially enabling more accurate and efficient GNN deployments.
- · AI researchers
- · Companies using GNNs for large datasets
- · Cloud computing providers
- · Drug discovery platforms
- · Inefficient graph processing methods
- · Computationally-limited GNN applications
More efficient GNN training and inference will lead to broader adoption of this AI architecture.
Improved GNN performance could accelerate discoveries in fields like materials science and bioinformatics.
The development of highly scalable topology-preserving methods may inspire similar innovations in other computationally intensive AI domains.
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Read at arXiv cs.LG